From 75ce9592457ef125af50dca1642b9cf02fd76e73 Mon Sep 17 00:00:00 2001 From: yangyudong <916291030@qq.com> Date: Wed, 16 Apr 2025 16:35:00 +0800 Subject: [PATCH] =?UTF-8?q?remove=20=E8=B7=9D=E7=A6=BB=E6=8B=A5=E6=8C=A4?= =?UTF-8?q?=E5=BA=A6=EF=BC=8C=E6=96=B0=E5=A2=9Emc=E9=97=AE=E9=A2=98?= =?UTF-8?q?=E8=BF=90=E8=A1=8C=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../evolution_algorithms/multi_objective.py | 45 ++++--- .../problems/mc/multi_objective_evolution.log | 10 ++ .../20250416_151536/generation_0.json | 91 ++++++++++++++ .../20250416_151536/generation_1.json | 115 ++++++++++++++++++ .../20250416_151536/generation_2.json | 99 +++++++++++++++ .../20250416_151536/generation_3.json | 115 ++++++++++++++++++ .../20250416_151536/generation_4.json | 83 +++++++++++++ .../20250416_151536/generation_5.json | 83 +++++++++++++ .../20250416_151536/generation_6.json | 83 +++++++++++++ .../20250416_151536/generation_7.json | 91 ++++++++++++++ .../20250416_151536/generation_8.json | 91 ++++++++++++++ .../20250416_151536/generation_9.json | 99 +++++++++++++++ 12 files changed, 981 insertions(+), 24 deletions(-) create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_0.json create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_1.json create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_2.json create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_3.json create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_4.json create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_5.json create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_6.json create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_7.json create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_8.json create mode 100644 src/lead/problems/mc/multi_objective_history/20250416_151536/generation_9.json diff --git a/src/lead/core/evolution_algorithms/multi_objective.py b/src/lead/core/evolution_algorithms/multi_objective.py index 52f9bdb..33cfc99 100644 --- a/src/lead/core/evolution_algorithms/multi_objective.py +++ b/src/lead/core/evolution_algorithms/multi_objective.py @@ -21,26 +21,18 @@ class MultiObjectiveEvolution(BaseEvolutionAlgorithm): llm_client, self.verify_operator) self.tournament_size = config.get("tournament_size", 3) - def select(self, population: List[MultiObjectiveIndividual], - num_parents: int) -> List[MultiObjectiveIndividual]: - """基于非支配排序和拥挤距离的锦标赛选择""" - # 先进行非支配排序 + def select(self, population: List[MultiObjectiveIndividual], + num_parents: int) -> List[MultiObjectiveIndividual]: + """基于纯非支配排序的锦标赛选择""" + # 进行非支配排序 fronts = self._non_dominated_sort(population) - # 计算拥挤距离 - for front in fronts: - for ind in front: - ind.crowding_distance(front) - # 锦标赛选择 parents = [] for _ in range(num_parents): candidates = random.sample(population, min(self.tournament_size, len(population))) - # 优先选择前沿等级高的，其次选择拥挤距离大的 - winner = min(candidates, key=lambda x: ( - x.rank, - -x.crowding_distance(fronts[x.rank]) - )) + # 仅根据前沿等级选择 + winner = min(candidates, key=lambda x: x.rank) parents.append(winner) return parents @@ -81,27 +73,32 @@ class MultiObjectiveEvolution(BaseEvolutionAlgorithm): ) return individual - def survive(self, population: List[MultiObjectiveIndividual], - offspring: List[MultiObjectiveIndividual], + def survive(self, population: List[MultiObjectiveIndividual], + offspring: List[MultiObjectiveIndividual], pop_size: int) -> List[MultiObjectiveIndividual]: - """基于非支配排序和拥挤距离的精英保留策略""" + """基于纯非支配排序的精英保留策略""" combined_pop = population + offspring # 非支配排序 fronts = self._non_dominated_sort(combined_pop) - # 构建新种群 + # 按前沿层级填充种群 new_pop = [] for front in fronts: - if len(new_pop) + len(front) <= pop_size: + if len(new_pop) < pop_size: new_pop.extend(front) else: - # 计算拥挤距离并选择 - for ind in front: - ind.crowding_distance(front) - front = sorted(front, key=lambda x: x.crowding_distance(front), reverse=True) - new_pop.extend(front[:pop_size - len(new_pop)]) break + + # 如果前沿个体不够，继续从下一层取 + if len(new_pop) < pop_size: + remaining = pop_size - len(new_pop) + for front in fronts[len(new_pop):]: + if remaining <= 0: + break + new_pop.extend(front[:remaining]) + remaining -= len(front) + return new_pop[:pop_size] def _non_dominated_sort(self, population: List[MultiObjectiveIndividual]) -> List[List[MultiObjectiveIndividual]]: diff --git a/src/lead/problems/mc/multi_objective_evolution.log b/src/lead/problems/mc/multi_objective_evolution.log index ed3dfe6..0e3b943 100644 --- a/src/lead/problems/mc/multi_objective_evolution.log +++ b/src/lead/problems/mc/multi_objective_evolution.log @@ -1 +1,11 @@ Generation 0000 | Pareto Front Size: 1 | Timestamp: 2025-04-16T01:20:59.478238 +Generation 0000 | Pareto Front Size: 3 | Timestamp: 2025-04-16T15:20:24.296920 +Generation 0001 | Pareto Front Size: 5 | Timestamp: 2025-04-16T15:21:20.873092 +Generation 0002 | Pareto Front Size: 3 | Timestamp: 2025-04-16T15:23:24.293834 +Generation 0003 | Pareto Front Size: 5 | Timestamp: 2025-04-16T15:24:37.459385 +Generation 0004 | Pareto Front Size: 1 | Timestamp: 2025-04-16T15:26:28.544849 +Generation 0005 | Pareto Front Size: 1 | Timestamp: 2025-04-16T15:30:30.213218 +Generation 0006 | Pareto Front Size: 1 | Timestamp: 2025-04-16T15:36:01.517114 +Generation 0007 | Pareto Front Size: 2 | Timestamp: 2025-04-16T15:40:15.364620 +Generation 0008 | Pareto Front Size: 2 | Timestamp: 2025-04-16T15:47:35.288547 +Generation 0009 | Pareto Front Size: 3 | Timestamp: 2025-04-16T15:51:29.712877 diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_0.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_0.json new file mode 100644 index 0000000..54a27af --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_0.json @@ -0,0 +1,91 @@ +{ + "generation": 0, + "population": [ + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9745118618011475 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n def optimize_color_count():\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(sorted(used_colors))}\n for i in range(n):\n if colors[i] in color_map:\n colors[i] = color_map[colors[i]]\n \n optimize_color_count()\n \n return colors", + "generation": 1, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0744099617004395 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9745118618011475 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 33, + "execution_time": 1.0532798767089844 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n \n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 0, + "fitnesses": { + "color_count": 29, + "execution_time": 1.081273078918457 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9857227802276611 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = True\n\n for vertex in range(n):\n if colors[vertex] == -1:\n assign_color(vertex)\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 1.9481892585754395 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9745118618011475 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n def optimize_color_count():\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(sorted(used_colors))}\n for i in range(n):\n if colors[i] in color_map:\n colors[i] = color_map[colors[i]]\n \n optimize_color_count()\n \n return colors", + "generation": 1, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0744099617004395 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9745118618011475 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_1.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_1.json new file mode 100644 index 0000000..57fcdec --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_1.json @@ -0,0 +1,115 @@ +{ + "generation": 1, + "population": [ + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9988811016082764 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9988811016082764 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9988811016082764 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9988811016082764 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 1.026442050933838 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n \n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 0, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0927319526672363 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n def optimize_color_count():\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(sorted(used_colors))}\n for i in range(n):\n if colors[i] in color_map:\n colors[i] = color_map[colors[i]]\n \n optimize_color_count()\n \n return colors", + "generation": 1, + "fitnesses": { + "color_count": 29, + "execution_time": 1.149785041809082 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9988811016082764 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9988811016082764 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9988811016082764 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9988811016082764 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_2.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_2.json new file mode 100644 index 0000000..2c333ed --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_2.json @@ -0,0 +1,99 @@ +{ + "generation": 2, + "population": [ + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 1.026442050933838 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n color_map = {}\n\n order = list(range(n))\n random.shuffle(order)\n\n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n chosen_color = None\n min_used = float('inf')\n\n for color in range(n):\n if available_colors[color]:\n used_count = color_map.get(color, 0)\n if used_count < min_used:\n min_used = used_count\n chosen_color = color\n\n if chosen_color is not None:\n colors[vertex] = chosen_color\n color_map[chosen_color] = color_map.get(chosen_color, 0) + 1\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 1000, + "execution_time": 1.0046441555023193 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n greedy_coloring()\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.087792158126831 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 32, + "execution_time": 1.035233974456787 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = len(adj_matrix)\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n \n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 0, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0927319526672363 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def optimized_greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n optimized_greedy_coloring()\n \n return colors", + "generation": 3, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0933640003204346 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def assign_color(vertex):\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n def greedy_coloring():\n for vertex in vertices:\n if colors[vertex] == -1:\n assign_color(vertex)\n \n greedy_coloring()\n \n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0939011573791504 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 1.026442050933838 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n color_map = {}\n\n order = list(range(n))\n random.shuffle(order)\n\n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n chosen_color = None\n min_used = float('inf')\n\n for color in range(n):\n if available_colors[color]:\n used_count = color_map.get(color, 0)\n if used_count < min_used:\n min_used = used_count\n chosen_color = color\n\n if chosen_color is not None:\n colors[vertex] = chosen_color\n color_map[chosen_color] = color_map.get(chosen_color, 0) + 1\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 1000, + "execution_time": 1.0046441555023193 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n greedy_coloring()\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.087792158126831 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_3.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_3.json new file mode 100644 index 0000000..cbe0c56 --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_3.json @@ -0,0 +1,115 @@ +{ + "generation": 3, + "population": [ + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.9886589050292969 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.9886589050292969 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.9886589050292969 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n order = list(range(n))\n random.shuffle(order)\n\n def optimized_color_selection(vertex):\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n return color\n return -1\n\n for vertex in order:\n colors[vertex] = optimized_color_selection(vertex)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 1.004497766494751 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n color_map = {}\n\n order = list(range(n))\n random.shuffle(order)\n\n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n chosen_color = None\n min_used = float('inf')\n\n for color in range(n):\n if available_colors[color]:\n used_count = color_map.get(color, 0)\n if used_count < min_used:\n min_used = used_count\n chosen_color = color\n\n if chosen_color is not None:\n colors[vertex] = chosen_color\n color_map[chosen_color] = color_map.get(chosen_color, 0) + 1\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 1000, + "execution_time": 1.0046441555023193 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n order = list(range(n))\n random.shuffle(order)\n \n for vertex in order:\n available_colors = [True] * n\n \n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n return colors", + "generation": 0, + "fitnesses": { + "color_count": 32, + "execution_time": 1.035233974456787 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.9886589050292969 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.9886589050292969 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.9886589050292969 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n order = list(range(n))\n random.shuffle(order)\n\n def optimized_color_selection(vertex):\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color]:\n return color\n return -1\n\n for vertex in order:\n colors[vertex] = optimized_color_selection(vertex)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 1.004497766494751 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_4.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_4.json new file mode 100644 index 0000000..436cd7e --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_4.json @@ -0,0 +1,83 @@ +{ + "generation": 4, + "population": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 0.5790848731994629 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9938340187072754 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.975269079208374 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0908830165863037 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n color_mapping = {}\n next_color = 0\n \n for vertex in vertices:\n available_colors = set(range(n))\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors.discard(colors[neighbor])\n chosen_color = min(available_colors)\n colors[vertex] = chosen_color\n \n if chosen_color not in color_mapping:\n color_mapping[chosen_color] = next_color\n next_color += 1\n colors[vertex] = color_mapping[chosen_color]\n \n optimized_coloring()\n\n unique_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(unique_colors)}\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.1260502338409424 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n order = list(range(n))\n random.shuffle(order)\n\n def optimized_color_selection(vertex):\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n min_color = n\n for color in range(n):\n if available_colors[color]:\n min_color = min(min_color, color)\n return min_color if min_color < n else -1\n\n for vertex in order:\n chosen_color = optimized_color_selection(vertex)\n colors[vertex] = chosen_color\n\n color_count = len(set(colors))\n improvement = True\n\n while improvement:\n improvement = False\n for vertex in order:\n current_color = colors[vertex]\n if current_color == -1:\n continue\n \n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(n):\n if available_colors[color] and color != current_color:\n colors[vertex] = color\n new_color_count = len(set(colors))\n if new_color_count < color_count:\n color_count = new_color_count\n improvement = True\n else:\n colors[vertex] = current_color\n break\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 30, + "execution_time": 2.031809091567993 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 0.5790848731994629 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_5.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_5.json new file mode 100644 index 0000000..836b683 --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_5.json @@ -0,0 +1,83 @@ +{ + "generation": 5, + "population": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 0.5790848731994629 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 32, + "execution_time": 1.0035450458526611 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.975269079208374 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0908830165863037 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 34, + "execution_time": 0.9917922019958496 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n # Optimize color assignment to minimize color_count\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n # Remap colors to ensure they are contiguous and minimize color_count\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(sorted(used_colors))}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 3, + "fitnesses": { + "color_count": 29, + "execution_time": 1.1028988361358643 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 0.5790848731994629 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_6.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_6.json new file mode 100644 index 0000000..a4a1034 --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_6.json @@ -0,0 +1,83 @@ +{ + "generation": 6, + "population": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 0.5790848731994629 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9685137271881104 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0908830165863037 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 32, + "execution_time": 1.0035450458526611 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.975269079208374 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 34, + "execution_time": 0.9917922019958496 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 0.5790848731994629 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_7.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_7.json new file mode 100644 index 0000000..3829858 --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_7.json @@ -0,0 +1,91 @@ +{ + "generation": 7, + "population": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9685137271881104 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0908830165863037 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 32, + "execution_time": 1.0035450458526611 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.975269079208374 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.6299710273742676 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 34, + "execution_time": 0.9917922019958496 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9685137271881104 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_8.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_8.json new file mode 100644 index 0000000..b940843 --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_8.json @@ -0,0 +1,91 @@ +{ + "generation": 8, + "population": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9685137271881104 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 32, + "execution_time": 1.0035450458526611 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 33, + "execution_time": 0.975269079208374 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.6299710273742676 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 34, + "execution_time": 0.9917922019958496 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n current_color = 0\n color_count = 0\n for vertex in vertices:\n available_colors = [True] * (color_count + 1)\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n \n for color in range(len(available_colors)):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n if colors[vertex] == color_count:\n color_count += 1\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 3.362344980239868 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 0.9685137271881104 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file diff --git a/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_9.json b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_9.json new file mode 100644 index 0000000..d211020 --- /dev/null +++ b/src/lead/problems/mc/multi_objective_history/20250416_151536/generation_9.json @@ -0,0 +1,99 @@ +{ + "generation": 9, + "population": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 32, + "execution_time": 1.0035450458526611 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 34, + "execution_time": 0.9917922019958496 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n \n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n \n def greedy_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n \n greedy_coloring()\n \n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0919930934906006 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 32, + "execution_time": 2.6567630767822266 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n used_colors = set(colors)\n color_map = {old_color: new_color for new_color, old_color in enumerate(used_colors)}\n colors = [color_map[color] for color in colors]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 29, + "execution_time": 1.6299710273742676 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n \n def minimize_colors():\n unique_colors = set(colors)\n color_map = {color: idx for idx, color in enumerate(unique_colors)}\n \n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_map[colors[i]]\n \n new_unique_colors = set(colors)\n new_color_count = len(new_unique_colors)\n \n color_count = new_color_count\n for i in range(n):\n for color in range(color_count):\n if color not in new_unique_colors:\n colors[i] = color\n break\n new_unique_colors = set(colors)\n if len(new_unique_colors) < color_count:\n color_count = len(new_unique_colors)\n \n minimize_colors()\n \n return colors", + "generation": 1, + "fitnesses": { + "color_count": 31, + "execution_time": 2.893918037414551 + } + }, + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n final_colors = [-1] * n\n color_mapping = {}\n next_color = 0\n\n for i in range(n):\n if colors[i] != -1:\n if colors[i] not in color_mapping:\n color_mapping[colors[i]] = next_color\n next_color += 1\n final_colors[i] = color_mapping[colors[i]]\n\n return final_colors", + "generation": 5, + "fitnesses": { + "color_count": 29, + "execution_time": 2.8868300914764404 + } + } + ], + "pareto_front": [ + { + "code": "import numpy as np\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n degree = [sum(row) for row in adj_matrix]\n vertices = list(range(n))\n vertices.sort(key=lambda x: -degree[x])\n\n def can_color(vertex, color):\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] == color:\n return False\n return True\n\n def optimized_coloring():\n for vertex in vertices:\n available_colors = [True] * n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n for color in range(n):\n if available_colors[color]:\n colors[vertex] = color\n break\n\n optimized_coloring()\n\n unique_colors = set(colors)\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(len(unique_colors)):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 4, + "fitnesses": { + "color_count": 29, + "execution_time": 1.0881679058074951 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n available_colors = [True] * n\n\n def assign_color(vertex):\n for i in range(n):\n available_colors[i] = True\n\n for neighbor in range(n):\n if adj_matrix[vertex][neighbor] == 1 and colors[neighbor] != -1:\n available_colors[colors[neighbor]] = False\n\n color = 0\n while color < n:\n if available_colors[color]:\n colors[vertex] = color\n break\n color += 1\n return colors[vertex]\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n assign_color(v)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n return colors", + "generation": 1, + "fitnesses": { + "color_count": 32, + "execution_time": 1.0035450458526611 + } + }, + { + "code": "import numpy as np\nimport random\n\ndef graph_coloring(adj_matrix):\n n = adj_matrix.shape[0]\n colors = [-1] * n\n\n def assign_color(vertex, used_colors):\n for color in range(n):\n if color not in used_colors:\n colors[vertex] = color\n return color\n return -1\n\n vertices = list(range(n))\n random.shuffle(vertices)\n\n for v in vertices:\n if colors[v] == -1:\n used_colors = set()\n for neighbor in range(n):\n if adj_matrix[v][neighbor] == 1 and colors[neighbor] != -1:\n used_colors.add(colors[neighbor])\n assign_color(v, used_colors)\n\n unique_colors = set(colors)\n color_count = len(unique_colors)\n\n for i in range(n):\n if colors[i] != -1:\n for new_color in range(color_count + 1):\n if new_color not in unique_colors:\n colors[i] = new_color\n break\n unique_colors = set(colors)\n\n color_usage = {color: 0 for color in unique_colors}\n for color in colors:\n if color != -1:\n color_usage[color] += 1\n\n color_mapping = {}\n new_color_index = 0\n for color in sorted(color_usage, key=color_usage.get):\n color_mapping[color] = new_color_index\n new_color_index += 1\n\n for i in range(n):\n if colors[i] != -1:\n colors[i] = color_mapping[colors[i]]\n\n return colors", + "generation": 2, + "fitnesses": { + "color_count": 34, + "execution_time": 0.9917922019958496 + } + } + ], + "objective_names": [ + "color_count", + "execution_time" + ] +} \ No newline at end of file