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remove 距离拥挤度，新增mc问题运行结果

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This commit is contained in:
yangyudong 2025-04-16 16:35:00 +08:00
parent f576f8d02e
commit 75ce959245
12 changed files with 981 additions and 24 deletions
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37
src/lead/core/evolution_algorithms/multi_objective.py
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@ -23,24 +23,16 @@ class MultiObjectiveEvolution(BaseEvolutionAlgorithm):
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
"""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
@ -84,24 +76,29 @@ class MultiObjectiveEvolution(BaseEvolutionAlgorithm):
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]]:

10
src/lead/problems/mc/multi_objective_evolution.log
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@ -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

91
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_0.json Normal file
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@ -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"
]
}

115
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_1.json Normal file
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@ -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"
]
}

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

@ -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"
]
}

115
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_3.json Normal file
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@ -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"
]
}

83
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_4.json Normal file
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@ -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"
]
}

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

@ -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"
]
}

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

@ -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"
]
}

91
src/lead/problems/mc/multi_objective_history/20250416_151536/generation_7.json Normal file
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@ -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"
]
}

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

@ -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"
]
}

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

@ -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"
]
}