diff --git a/src/lead/core/evolution.py b/src/lead/core/evolution.py index 46e0d05..c8c1dda 100644 --- a/src/lead/core/evolution.py +++ b/src/lead/core/evolution.py @@ -40,20 +40,33 @@ class EvolutionEngine: def initialize_population(self, size: int) -> List[AlgorithmIndividual]: - """使用LLM生成初始种群""" + """改进后的种群初始化方法""" problem_config = self._load_problem_config() - individuals = [] - while len(individuals) < size: + # 首先生成多个不同的算法思路 + ideas = self.initialize_operator.ideas_generator.generate_ideas( + problem_config["description"], + size + ) + + # 基于每个思路生成具体实现 + individuals = [] + for i, idea in enumerate(ideas): code = self.initialize_operator.generate_initial_code( problem_config["description"], problem_config["function_name"], problem_config["input_format"], - problem_config["output_format"] + problem_config["output_format"], + idea ) - if code: # 只添加成功生成的代码 - individuals.append(AlgorithmIndividual(code, generation=0)) + if code: + individuals.append(AlgorithmIndividual( + code, + generation=0, + metadata={"idea": idea} # 保存原始思路 + )) + print(f"成功生成初始种群，包含{len(individuals)}个不同个体") return individuals def run_evolution(self, generations: int = None, population_size: int = None): diff --git a/src/lead/core/multi_objective/evolution.py b/src/lead/core/multi_objective/evolution.py index 2c696e7..abba534 100644 --- a/src/lead/core/multi_objective/evolution.py +++ b/src/lead/core/multi_objective/evolution.py @@ -35,20 +35,33 @@ class MultiObjectiveEvolutionEngine: print(f"多目标进化参数：{self.evolution_params}") def initialize_population(self, size: int) -> List[MultiObjectiveIndividual]: - """使用LLM生成初始种群""" + """改进后的种群初始化方法""" problem_config = self._load_problem_config() - population = [] - while len(population) < size: + # 首先生成多个不同的算法思路 + ideas = self.initialize_operator.ideas_generator.generate_ideas( + problem_config["description"], + size + ) + + # 基于每个思路生成具体实现 + population = [] + for i, idea in enumerate(ideas): code = self.initialize_operator.generate_initial_code( problem_config["description"], problem_config["function_name"], problem_config["input_format"], - problem_config["output_format"] + problem_config["output_format"], + idea ) if code: - population.append(MultiObjectiveIndividual(code, generation=0)) + population.append(MultiObjectiveIndividual( + code, + generation=0, + metadata={"idea": idea} # 保存原始思路 + )) + print(f"成功生成初始种群，包含{len(population)}个不同个体") return population def run_evolution(self, generations: int = None, population_size: int = None): diff --git a/src/lead/core/multi_objective/individual.py b/src/lead/core/multi_objective/individual.py index f71f289..b812efc 100644 --- a/src/lead/core/multi_objective/individual.py +++ b/src/lead/core/multi_objective/individual.py @@ -8,6 +8,7 @@ class MultiObjectiveIndividual: code: str generation: int fitnesses: Dict[str, float] = field(default_factory=dict) + metadata: Dict[str, Any] = field(default_factory=dict) def dominates(self, other: 'MultiObjectiveIndividual') -> bool: """检查当前个体是否Pareto支配另一个个体""" @@ -27,7 +28,8 @@ class MultiObjectiveIndividual: return { "code": self.code, "generation": self.generation, - "fitnesses": self.fitnesses + "fitnesses": self.fitnesses, + "metadata": self.metadata } @classmethod @@ -36,7 +38,8 @@ class MultiObjectiveIndividual: return cls( code=data["code"], generation=data["generation"], - fitnesses=data.get("fitnesses", {}) + fitnesses=data.get("fitnesses", {}), + metadata=data.get("metadata", {}) ) def crowding_distance(self, front: List['MultiObjectiveIndividual']) -> float: diff --git a/src/lead/core/operators/initialize_operator.py b/src/lead/core/operators/initialize_operator.py index aad191c..ab28c80 100644 --- a/src/lead/core/operators/initialize_operator.py +++ b/src/lead/core/operators/initialize_operator.py @@ -1,18 +1,44 @@ -from typing import Optional +from typing import Optional, List from ..llm_integration import LLMClient from .verify_operator import VerifyOperator +class AlgorithmIdeasGenerator: + def __init__(self, llm_client: LLMClient): + self.llm_client = llm_client + + def generate_ideas(self, problem_desc: str, num_ideas: int) -> List[str]: + """生成多个不同的算法思路""" + prompt = f"""请为以下问题生成{num_ideas}个不同的算法解决思路： +问题描述：{problem_desc} + +要求： +1. 每个思路用一行简洁描述 +2. 不要包含其他任何信息 +3. 思路之间应有明显差异 +4. 总共返回正好{num_ideas}行 + +示例格式： +思路1描述 +思路2描述 +... +思路{num_ideas}描述 +""" + response = self.llm_client._call_llm(prompt, operator="generate_ideas") + return [line.strip() for line in response.split('\n') if line.strip()] + class InitializeOperator: def __init__(self, llm_client: LLMClient): self.llm_client = llm_client + self.ideas_generator = AlgorithmIdeasGenerator(llm_client) self.verify_operator = VerifyOperator(llm_client) - def generate_initial_code(self, problem_desc: str, function_name: str, input_fmt: str, output_fmt: str) -> str: - """生成初始算法代码""" - prompt = f"""请用Python编写一个解决以下问题的函数： -问题描述：{problem_desc} + def generate_initial_code(self, problem_desc: str, function_name: str, + input_fmt: str, output_fmt: str, idea: str = None) -> str: + """基于特定思路生成初始代码""" + base_prompt = f"""请用Python实现以下算法思路： +思路：{idea or '默认算法'} -函数要求： +具体要求： 1. 函数名：{function_name} 2. 输入：{input_fmt} 3. 返回值：{output_fmt} @@ -20,11 +46,10 @@ class InitializeOperator: 注意： - 不需要添加类型注解 - 只返回函数代码，不要包含任何解释或测试用例 -- 我们允许返回的代码中包含了多个函数，但是一定要确直接运行{function_name}函数并将数据传入即可完成全部流程并获得预期的返回 -- 算法应该尽可能地创新，避免完全依赖已有的成熟算法，可以接受在成熟算法的基础上进行一定的修改 -- 算法应该尽可能地简洁，避免不必要的复杂性 +- 可以包含多个辅助函数 +- 代码应简洁高效 """ - code = self.llm_client._call_llm(prompt, operator="initialize") + code = self.llm_client._call_llm(base_prompt, operator="initialize") if code: return self.verify_operator.verify_code_format(code, function_name) return None \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_evolution.log b/src/lead/problems/mc/multi_objective_evolution.log deleted file mode 100644 index 0e3b943..0000000 --- a/src/lead/problems/mc/multi_objective_evolution.log +++ /dev/null @@ -1,11 +0,0 @@ -Generation 0000 | Pareto Front Size: 1 | Timestamp: 2025-04-16T01:20:59.478238 -Generation 0000 | Pareto Front Size: 3 | Timestamp: 2025-04-16T15:20:24.296920 -Generation 0001 | Pareto Front Size: 5 | Timestamp: 2025-04-16T15:21:20.873092 -Generation 0002 | Pareto Front Size: 3 | Timestamp: 2025-04-16T15:23:24.293834 -Generation 0003 | Pareto Front Size: 5 | Timestamp: 2025-04-16T15:24:37.459385 -Generation 0004 | Pareto Front Size: 1 | Timestamp: 2025-04-16T15:26:28.544849 -Generation 0005 | Pareto Front Size: 1 | Timestamp: 2025-04-16T15:30:30.213218 -Generation 0006 | Pareto Front Size: 1 | Timestamp: 2025-04-16T15:36:01.517114 -Generation 0007 | Pareto Front Size: 2 | Timestamp: 2025-04-16T15:40:15.364620 -Generation 0008 | Pareto Front Size: 2 | Timestamp: 2025-04-16T15:47:35.288547 -Generation 0009 | Pareto Front Size: 3 | Timestamp: 2025-04-16T15:51:29.712877 diff --git a/src/lead/problems/mc/multi_objective_history/20250416_010845/generation_0.json b/src/lead/problems/mc/multi_objective_history/20250416_010845/generation_0.json deleted file mode 100644 index 3d68bce..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_010845/generation_0.json +++ /dev/null @@ -1,83 +0,0 @@ -{ - "generation": 0, - "population": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available = [False] * n\n \n def get_neighbor_colors(node):\n for neighbor in range(n):\n if adj_matrix[node][neighbor] == 1 and colors[neighbor] != -1:\n available[colors[neighbor]] = True\n\n def assign_color(node):\n get_neighbor_colors(node)\n color = 0\n while color < n:\n if not available[color]:\n colors[node] = color\n break\n color += 1\n for neighbor in range(n):\n if adj_matrix[node][neighbor] == 1 and colors[neighbor] != -1:\n available[colors[neighbor]] = False\n \n for node in range(n):\n if colors[node] == -1:\n assign_color(node)\n available = [False] * n\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 1.0018470287322998 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n \n vertex_order = list(range(n))\n random.shuffle(vertex_order)\n \n for vertex in vertex_order:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n color = 0\n while color < n and not available_colors[color]:\n color += 1\n \n colors[vertex] = color\n \n return colors", - "generation": 0, - "fitnesses": { - "color_count": 32, - "execution_time": 1.5306849479675293 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n available_colors = [True] * n\n\n def greedy_coloring():\n colors[0] = 0\n\n for u in range(1, n):\n available_colors[:] = [True] * n\n \n for v in range(n):\n if adj_matrix[u][v] == 1 and colors[v] != -1:\n available_colors[colors[v]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[u] = color\n break\n\n greedy_coloring()\n \n def optimize_colors():\n color_count = max(colors) + 1\n color_mapping = {i: [] for i in range(color_count)}\n \n for vertex in range(n):\n if colors[vertex] != -1:\n color_mapping[colors[vertex]].append(vertex)\n \n for color in range(color_count):\n if not color_mapping[color]:\n continue\n for new_color in range(color + 1, color_count):\n if new_color not in color_mapping:\n continue\n is_codable = True\n for v1 in color_mapping[color]:\n for v2 in color_mapping[new_color]:\n if adj_matrix[v1][v2] == 1:\n is_codable = False\n break\n if not is_codable:\n break\n if is_codable:\n for v in color_mapping[new_color]:\n colors[v] = color\n del color_mapping[new_color]\n break\n\n optimize_colors()\n \n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 1.539431095123291 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n available_colors = [False] * n\n \n def find_available_color(vertex):\n for i in range(n):\n available_colors[i] = True\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n return color\n return n\n\n for vertex in range(n):\n colors[vertex] = find_available_color(vertex)\n \n def optimize_coloring():\n unique_colors = sorted(set(colors))\n color_map = {old_color: new_color for new_color, old_color in enumerate(unique_colors)}\n for i in range(n):\n colors[i] = color_map[colors[i]]\n\n optimize_coloring()\n \n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 2.813322067260742 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n \n def get_available_colors(vertex):\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n return available_colors\n\n vertex_order = list(range(n))\n random.shuffle(vertex_order)\n \n for vertex in vertex_order:\n available_colors = get_available_colors(vertex)\n \n color = 0\n while color < n and not available_colors[color]:\n color += 1\n \n if color == n:\n min_color_used = min([c for c in colors if c != -1], default=0)\n color = min_color_used + 1\n \n colors[vertex] = color\n \n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 2.3673529624938965 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n \n def assign_colors():\n for node in range(n):\n if colors[node] == -1:\n reset_available()\n for neighbor in range(n):\n if adj_matrix[node][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n colors[node] = get_first_available_color()\n \n def reset_available():\n for i in range(n):\n available_colors[i] = True\n\n def get_first_available_color():\n for color in range(n):\n if available_colors[color]:\n return color\n return n\n\n def local_search():\n for node in range(n):\n current_color = colors[node]\n for try_color in range(n):\n if try_color != current_color and is_valid_color_assignment(node, try_color):\n colors[node] = try_color\n break\n\n def is_valid_color_assignment(node, color):\n for neighbor in range(n):\n if adj_matrix[node][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n assign_colors()\n \n for _ in range(n):\n local_search()\n \n return colors", - "generation": 0, - "fitnesses": { - "color_count": Infinity, - "execution_time": Infinity - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n available_colors = [True] * n\n\n def is_color_possible(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def greedy_coloring():\n for vertex in range(n):\n available_colors[:] = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n\n def local_search():\n improved = True\n while improved:\n improved = False\n for vertex in range(n):\n current_color = colors[vertex]\n conflict_colors = set()\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1:\n conflict_colors.add(colors[neighbor])\n for new_color in range(len(conflict_colors) + 1):\n if new_color not in conflict_colors:\n if new_color != current_color:\n colors[vertex] = new_color\n improved = True\n break\n\n local_search()\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": Infinity, - "execution_time": Infinity - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n available_colors = [True] * n\n\n def greedy_coloring():\n colors[0] = 0\n\n for u in range(1, n):\n available_colors[:] = [True] * n\n \n for v in range(n):\n if adj_matrix[u][v] == 1 and colors[v] != -1:\n available_colors[colors[v]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[u] = color\n break\n\n greedy_coloring()\n \n def optimize_colors():\n color_count = max(colors) + 1\n color_mapping = {i: [] for i in range(color_count)}\n \n for vertex in range(n):\n if colors[vertex] != -1:\n color_mapping[colors[vertex]].append(vertex)\n \n merged = True\n while merged:\n merged = False\n for color in range(color_count):\n if not color_mapping[color]:\n continue\n for new_color in range(color + 1, color_count):\n if new_color not in color_mapping:\n continue\n is_codable = True\n for v1 in color_mapping[color]:\n for v2 in color_mapping[new_color]:\n if adj_matrix[v1][v2] == 1:\n is_codable = False\n break\n if not is_codable:\n break\n if is_codable:\n for v in color_mapping[new_color]:\n colors[v] = color\n del color_mapping[new_color]\n merged = True\n color_count -= 1\n break\n if merged:\n break\n\n optimize_colors()\n \n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 2.5284109115600586 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available = [False] * n\n \n def get_neighbor_colors(node):\n for neighbor in range(n):\n if adj_matrix[node][neighbor] == 1 and colors[neighbor] != -1:\n available[colors[neighbor]] = True\n\n def assign_color(node):\n get_neighbor_colors(node)\n color = 0\n while color < n:\n if not available[color]:\n colors[node] = color\n break\n color += 1\n for neighbor in range(n):\n if adj_matrix[node][neighbor] == 1 and colors[neighbor] != -1:\n available[colors[neighbor]] = False\n \n for node in range(n):\n if colors[node] == -1:\n assign_color(node)\n available = [False] * n\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 1.0018470287322998 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_0.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_0.json deleted file mode 100644 index 54a27af..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_0.json +++ /dev/null @@ -1,91 +0,0 @@ -{ - "generation": 0, - "population": [ - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9745118618011475 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n def optimize_color_count():\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(sorted(used_colors))}\n for i in range(n):\n if colors[i] in color_map:\n colors[i] = color_map[colors[i]]\n \n optimize_color_count()\n \n return colors", - "generation": 1, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0744099617004395 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9745118618011475 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 33, - "execution_time": 1.0532798767089844 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n \n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 0, - "fitnesses": { - "color_count": 29, - "execution_time": 1.081273078918457 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9857227802276611 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = True\n\n for vertex in range(n):\n if colors[vertex] == -1:\n assign_color(vertex)\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 1.9481892585754395 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9745118618011475 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n def optimize_color_count():\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(sorted(used_colors))}\n for i in range(n):\n if colors[i] in color_map:\n colors[i] = color_map[colors[i]]\n \n optimize_color_count()\n \n return colors", - "generation": 1, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0744099617004395 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9745118618011475 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_1.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_1.json deleted file mode 100644 index 57fcdec..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_1.json +++ /dev/null @@ -1,115 +0,0 @@ -{ - "generation": 1, - "population": [ - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9988811016082764 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9988811016082764 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9988811016082764 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9988811016082764 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 1.026442050933838 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n \n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 0, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0927319526672363 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n def optimize_color_count():\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(sorted(used_colors))}\n for i in range(n):\n if colors[i] in color_map:\n colors[i] = color_map[colors[i]]\n \n optimize_color_count()\n \n return colors", - "generation": 1, - "fitnesses": { - "color_count": 29, - "execution_time": 1.149785041809082 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9988811016082764 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9988811016082764 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9988811016082764 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9988811016082764 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_2.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_2.json deleted file mode 100644 index 2c333ed..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_2.json +++ /dev/null @@ -1,99 +0,0 @@ -{ - "generation": 2, - "population": [ - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 1.026442050933838 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n color_map = {}\n\n order = list(range(n))\n random.shuffle(order)\n\n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n chosen_color = None\n min_used = float('inf')\n\n for color in range(n):\n if available_colors[color]:\n used_count = color_map.get(color, 0)\n if used_count < min_used:\n min_used = used_count\n chosen_color = color\n\n if chosen_color is not None:\n colors[vertex] = chosen_color\n color_map[chosen_color] = color_map.get(chosen_color, 0) + 1\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 1000, - "execution_time": 1.0046441555023193 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n greedy_coloring()\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.087792158126831 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 32, - "execution_time": 1.035233974456787 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n \n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 0, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0927319526672363 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def optimized_greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n optimized_greedy_coloring()\n \n return colors", - "generation": 3, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0933640003204346 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def assign_color(vertex):\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n def greedy_coloring():\n for vertex in vertices:\n if colors[vertex] == -1:\n assign_color(vertex)\n \n greedy_coloring()\n \n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0939011573791504 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 1.026442050933838 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n color_map = {}\n\n order = list(range(n))\n random.shuffle(order)\n\n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n chosen_color = None\n min_used = float('inf')\n\n for color in range(n):\n if available_colors[color]:\n used_count = color_map.get(color, 0)\n if used_count < min_used:\n min_used = used_count\n chosen_color = color\n\n if chosen_color is not None:\n colors[vertex] = chosen_color\n color_map[chosen_color] = color_map.get(chosen_color, 0) + 1\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 1000, - "execution_time": 1.0046441555023193 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n greedy_coloring()\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.087792158126831 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_3.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_3.json deleted file mode 100644 index cbe0c56..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_3.json +++ /dev/null @@ -1,115 +0,0 @@ -{ - "generation": 3, - "population": [ - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.9886589050292969 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.9886589050292969 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.9886589050292969 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n order = list(range(n))\n random.shuffle(order)\n\n def optimized_color_selection(vertex):\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n return color\n return -1\n\n for vertex in order:\n colors[vertex] = optimized_color_selection(vertex)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 1.004497766494751 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n color_map = {}\n\n order = list(range(n))\n random.shuffle(order)\n\n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n chosen_color = None\n min_used = float('inf')\n\n for color in range(n):\n if available_colors[color]:\n used_count = color_map.get(color, 0)\n if used_count < min_used:\n min_used = used_count\n chosen_color = color\n\n if chosen_color is not None:\n colors[vertex] = chosen_color\n color_map[chosen_color] = color_map.get(chosen_color, 0) + 1\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 1000, - "execution_time": 1.0046441555023193 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", - "generation": 0, - "fitnesses": { - "color_count": 32, - "execution_time": 1.035233974456787 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.9886589050292969 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.9886589050292969 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.9886589050292969 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n order = list(range(n))\n random.shuffle(order)\n\n def optimized_color_selection(vertex):\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n return color\n return -1\n\n for vertex in order:\n colors[vertex] = optimized_color_selection(vertex)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 1.004497766494751 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_4.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_4.json deleted file mode 100644 index 436cd7e..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_4.json +++ /dev/null @@ -1,83 +0,0 @@ -{ - "generation": 4, - "population": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 0.5790848731994629 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9938340187072754 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.975269079208374 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0908830165863037 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n color_mapping = {}\n next_color = 0\n \n for vertex in vertices:\n available_colors = set(range(n))\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors.discard(colors[neighbor])\n chosen_color = min(available_colors)\n colors[vertex] = chosen_color\n \n if chosen_color not in color_mapping:\n color_mapping[chosen_color] = next_color\n next_color += 1\n colors[vertex] = color_mapping[chosen_color]\n \n optimized_coloring()\n\n unique_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(unique_colors)}\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.1260502338409424 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n order = list(range(n))\n random.shuffle(order)\n\n def optimized_color_selection(vertex):\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n min_color = n\n for color in range(n):\n if available_colors[color]:\n min_color = min(min_color, color)\n return min_color if min_color < n else -1\n\n for vertex in order:\n chosen_color = optimized_color_selection(vertex)\n colors[vertex] = chosen_color\n\n color_count = len(set(colors))\n improvement = True\n\n while improvement:\n improvement = False\n for vertex in order:\n current_color = colors[vertex]\n if current_color == -1:\n continue\n \n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color] and color != current_color:\n colors[vertex] = color\n new_color_count = len(set(colors))\n if new_color_count < color_count:\n color_count = new_color_count\n improvement = True\n else:\n colors[vertex] = current_color\n break\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 30, - "execution_time": 2.031809091567993 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 0.5790848731994629 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_5.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_5.json deleted file mode 100644 index 836b683..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_5.json +++ /dev/null @@ -1,83 +0,0 @@ -{ - "generation": 5, - "population": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 0.5790848731994629 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 32, - "execution_time": 1.0035450458526611 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.975269079208374 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0908830165863037 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 34, - "execution_time": 0.9917922019958496 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n # Optimize color assignment to minimize color_count\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n # Remap colors to ensure they are contiguous and minimize color_count\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(sorted(used_colors))}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 3, - "fitnesses": { - "color_count": 29, - "execution_time": 1.1028988361358643 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 0.5790848731994629 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_6.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_6.json deleted file mode 100644 index a4a1034..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_6.json +++ /dev/null @@ -1,83 +0,0 @@ -{ - "generation": 6, - "population": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 0.5790848731994629 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9685137271881104 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0908830165863037 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 32, - "execution_time": 1.0035450458526611 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.975269079208374 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 34, - "execution_time": 0.9917922019958496 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 0.5790848731994629 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_7.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_7.json deleted file mode 100644 index 3829858..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_7.json +++ /dev/null @@ -1,91 +0,0 @@ -{ - "generation": 7, - "population": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9685137271881104 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0908830165863037 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 32, - "execution_time": 1.0035450458526611 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.975269079208374 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.6299710273742676 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 34, - "execution_time": 0.9917922019958496 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9685137271881104 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_8.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_8.json deleted file mode 100644 index b940843..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_8.json +++ /dev/null @@ -1,91 +0,0 @@ -{ - "generation": 8, - "population": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9685137271881104 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 32, - "execution_time": 1.0035450458526611 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 33, - "execution_time": 0.975269079208374 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.6299710273742676 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 34, - "execution_time": 0.9917922019958496 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 3.362344980239868 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 0.9685137271881104 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_9.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_9.json deleted file mode 100644 index d211020..0000000 --- a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_9.json +++ /dev/null @@ -1,99 +0,0 @@ -{ - "generation": 9, - "population": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 32, - "execution_time": 1.0035450458526611 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 34, - "execution_time": 0.9917922019958496 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0919930934906006 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 32, - "execution_time": 2.6567630767822266 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 29, - "execution_time": 1.6299710273742676 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", - "generation": 1, - "fitnesses": { - "color_count": 31, - "execution_time": 2.893918037414551 - } - }, - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n final_colors = [-1] * n\n color_mapping = {}\n next_color = 0\n\n for i in range(n):\n if colors[i] != -1:\n if colors[i] not in color_mapping:\n color_mapping[colors[i]] = next_color\n next_color += 1\n final_colors[i] = color_mapping[colors[i]]\n\n return final_colors", - "generation": 5, - "fitnesses": { - "color_count": 29, - "execution_time": 2.8868300914764404 - } - } - ], - "pareto_front": [ - { - "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 4, - "fitnesses": { - "color_count": 29, - "execution_time": 1.0881679058074951 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", - "generation": 1, - "fitnesses": { - "color_count": 32, - "execution_time": 1.0035450458526611 - } - }, - { - "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", - "generation": 2, - "fitnesses": { - "color_count": 34, - "execution_time": 0.9917922019958496 - } - } - ], - "objective_names": [ - "color_count", - "execution_time" - ] -} \ No newline at end of file