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初始化算子修改第一版，多目标进化过程待修改

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This commit is contained in:
yangyudong 2025-04-17 01:52:15 +08:00
parent 75ce959245
commit 47fc9e77a1
16 changed files with 77 additions and 1067 deletions
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25
src/lead/core/evolution.py
View File

@ -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):

23
src/lead/core/multi_objective/evolution.py
View File

@ -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):

7
src/lead/core/multi_objective/individual.py
View File

@ -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:

45
src/lead/core/operators/initialize_operator.py
View File

@ -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

11
src/lead/problems/mc/multi_objective_evolution.log
View File

@ -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

83
src/lead/problems/mc/multi_objective_history/20250416_010845/generation_0.json
View File

@ -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"
]
}

91
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_0.json
View File

@ -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"
]
}

115
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_1.json
View File

@ -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"
]
}

99
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_2.json
View File

@ -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"
]
}

115
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_3.json
View File

@ -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"
]
}

83
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_4.json
View File

@ -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"
]
}

83
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_5.json
View File

@ -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"
]
}

83
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_6.json
View File

@ -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"
]
}

91
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_7.json
View File

@ -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"
]
}

91
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_8.json
View File

@ -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"
]
}

99
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_9.json
View File

@ -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"
]
}