第一个可用的多目标进化策略

src/lead/config/settings.py

@@ -1,4 +1,5 @@
 import os
+from typing import Dict, Any, List
 
 # 项目根目录
 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
@@ -16,10 +17,35 @@ DEFAULT_LLM_CONFIG = {
     "max_tokens": 1000
 }
 
+# 默认问题配置模板
+DEFAULT_PROBLEM_CONFIG = {
+    "description": "问题描述",
+    "function_name": "solution",
+    "input_format": "输入格式描述",
+    "output_format": "输出格式描述",
+    "evaluation_timeout": 30,
+    "multi_objective": False,  # 是否使用多目标优化
+    "objective_names": ["f1", "f2"],  # 多目标名称列表
+    "llm_config": DEFAULT_LLM_CONFIG,
+    "evolution_params": {
+        "algorithm": "TU",  # TU: Tournament, DE: Differential Evolution, MO: Multi-Objective
+        "population_size": 8,
+        "generations": 10,
+        "mutation_rate": 0.3,
+        "crossover_rate": 0.5,
+        "tournament_size": 3,
+        "F": 0.5,  # DE参数
+        "CR": 0.7  # DE参数
+    }
+}
+
 # 默认进化参数
 DEFAULT_EVOLUTION_PARAMS = {
     "population_size": 8,
     "generations": 10,
     "mutation_rate": 0.3,
-    "crossover_rate": 0.5
+    "crossover_rate": 0.5,
+    "tournament_size": 3,
+    "F": 0.5,  # DE参数
+    "CR": 0.7  # DE参数
 }

src/lead/core/evolution_algorithms/__init__.py

@@ -1,4 +1,5 @@
 from .tournament import TournamentEvolution
 from .base import BaseEvolutionAlgorithm
+from .multi_objective import MultiObjectiveEvolution
 
-__all__ = ['TournamentEvolution', 'BaseEvolutionAlgorithm'] 
+__all__ = ['TournamentEvolution', 'BaseEvolutionAlgorithm', 'MultiObjectiveEvolution']

src/lead/core/evolution_algorithms/multi_objective.py

@@ -0,0 +1,134 @@
+from typing import List
+import random
+import numpy as np
+from .base import BaseEvolutionAlgorithm
+from ..multi_objective.individual import MultiObjectiveIndividual
+from ..llm_integration import LLMClient
+from ..operators.multi_objective_crossover import MultiObjectiveCrossoverOperator
+from ..operators.multi_objective_mutation import MultiObjectiveMutationOperator
+from ..operators.verify_operator import VerifyOperator
+
+class MultiObjectiveEvolution(BaseEvolutionAlgorithm):
+    """多目标进化算法，基于NSGA-II框架"""
+    
+    def __init__(self, config: dict, llm_client: LLMClient):
+        super().__init__(config)
+        self.llm_client = llm_client
+        self.verify_operator = VerifyOperator(llm_client)
+        self.crossover_operator = MultiObjectiveCrossoverOperator(
+            llm_client, self.verify_operator)
+        self.mutation_operator = MultiObjectiveMutationOperator(
+            llm_client, self.verify_operator)
+        self.tournament_size = config.get("tournament_size", 3)
+    
+    def select(self, population: List[MultiObjectiveIndividual], 
+              num_parents: int) -> List[MultiObjectiveIndividual]:
+        """基于非支配排序和拥挤距离的锦标赛选择"""
+        # 先进行非支配排序
+        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])
+            ))
+            parents.append(winner)
+        return parents
+    
+    def crossover(self, parents: List[MultiObjectiveIndividual]) -> List[MultiObjectiveIndividual]:
+        """多目标交叉操作"""
+        if len(parents) < 2:
+            return parents
+            
+        if random.random() < self.config.get("crossover_rate", 0.9):
+            # 获取目标名称（从第一个父代中获取）
+            objective_names = list(parents[0].fitnesses.keys())
+            
+            # 执行多目标交叉
+            child_code, _ = self.crossover_operator.crossover(
+                parents[0], parents[1], objective_names)
+            
+            if child_code:
+                return [MultiObjectiveIndividual(
+                    child_code, 
+                    generation=parents[0].generation + 1
+                )]
+        return [parents[0]]
+    
+    def mutate(self, individual: MultiObjectiveIndividual) -> MultiObjectiveIndividual:
+        """多目标变异操作"""
+        if random.random() < self.config.get("mutation_rate", 0.1):
+            # 获取目标名称
+            objective_names = list(individual.fitnesses.keys())
+            
+            # 执行多目标变异
+            mutated_code, _ = self.mutation_operator.mutate(
+                individual, objective_names)
+            
+            if mutated_code:
+                return MultiObjectiveIndividual(
+                    mutated_code,
+                    generation=individual.generation
+                )
+        return individual
+    
+    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"""
+        combined_pop = population + offspring
+        
+        # 非支配排序
+        fronts = self._non_dominated_sort(combined_pop)
+        
+        # 构建新种群
+        new_pop = []
+        for front in fronts:
+            if len(new_pop) + len(front) <= 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
+        return new_pop[:pop_size]
+    
+    def _non_dominated_sort(self, population: List[MultiObjectiveIndividual]) -> List[List[MultiObjectiveIndividual]]:
+        """非支配排序"""
+        fronts = [[]]
+        for ind in population:
+            ind.domination_count = 0
+            ind.dominated_set = []
+            for other in population:
+                if ind.dominates(other):
+                    ind.dominated_set.append(other)
+                elif other.dominates(ind):
+                    ind.domination_count += 1
+            if ind.domination_count == 0:
+                fronts[0].append(ind)
+                ind.rank = 0
+        
+        i = 0
+        while fronts[i]:
+            next_front = []
+            for ind in fronts[i]:
+                for dominated_ind in ind.dominated_set:
+                    dominated_ind.domination_count -= 1
+                    if dominated_ind.domination_count == 0:
+                        dominated_ind.rank = i + 1
+                        next_front.append(dominated_ind)
+            i += 1
+            fronts.append(next_front)
+        
+        return fronts[:-1]  # 最后一个是空的

src/lead/core/multi_objective/evolution.py

@@ -1,12 +1,13 @@
 import os
 import json
-import numpy as np
+from typing import List
-from typing import List, Dict, Any
 from ...utils.multi_objective.evaluator import MultiObjectiveEvaluator
 from ...utils.multi_objective.storage import MultiObjectiveGenerationStorage
 from .individual import MultiObjectiveIndividual
 from ..llm_integration import LLMClient
 from ...config.settings import DEFAULT_EVOLUTION_PARAMS
+from ..evolution_algorithms.multi_objective import MultiObjectiveEvolution
+from ..operators.initialize_operator import InitializeOperator
 
 class MultiObjectiveEvolutionEngine:
     """多目标进化引擎"""
@@ -16,6 +17,7 @@ class MultiObjectiveEvolutionEngine:
         self.storage = MultiObjectiveGenerationStorage(problem_path)
         self.evaluator = MultiObjectiveEvaluator(problem_path)
         self.llm_client = LLMClient.from_config(problem_path)
+        self.initialize_operator = InitializeOperator(self.llm_client)
         
         # 加载进化参数
         config = self._load_problem_config()
@@ -23,16 +25,30 @@ class MultiObjectiveEvolutionEngine:
             **DEFAULT_EVOLUTION_PARAMS,
             **config.get("evolution_params", {})
         }
+        
+        # 初始化多目标进化算法
+        self.evolution_algorithm = MultiObjectiveEvolution(
+            self.evolution_params,
+            self.llm_client
+        )
+        
         print(f"多目标进化参数：{self.evolution_params}")
     
     def initialize_population(self, size: int) -> List[MultiObjectiveIndividual]:
-        """初始化种群"""
+        """使用LLM生成初始种群"""
-        # 这里可以集成LLM生成初始代码
+        problem_config = self._load_problem_config()
         population = []
-        for _ in range(size):
+        
-            # 示例代码，实际应使用LLM生成
+        while len(population) < size:
-            code = "def solution(input):\n    return {'f1': 0.0, 'f2': 0.0}"
+            code = self.initialize_operator.generate_initial_code(
-            population.append(MultiObjectiveIndividual(code, generation=0))
+                problem_config["description"],
+                problem_config["function_name"],
+                problem_config["input_format"],
+                problem_config["output_format"]
+            )
+            if code:
+                population.append(MultiObjectiveIndividual(code, generation=0))
+        
         return population
     
     def run_evolution(self, generations: int = None, population_size: int = None):
@@ -54,18 +70,19 @@ class MultiObjectiveEvolutionEngine:
             print(f"\n开始第 {gen+1}/{generations} 代进化")
             
             # 生成子代
-            offspring = self._generate_offspring(population)
+            offspring = []
+            while len(offspring) < population_size:
+                parents = self.evolution_algorithm.select(population, 2)
+                children = self.evolution_algorithm.crossover(parents)
+                for child in children:
+                    mutated_child = self.evolution_algorithm.mutate(child)
+                    if mutated_child.code:
+                        mutated_child.fitnesses = self.evaluator.evaluate(mutated_child.code)
+                        print(f"新个体适应度：{mutated_child.fitnesses}")
+                        offspring.append(mutated_child)
             
-            # 评估子代
+            # 生存选择
-            for ind in offspring:
+            population = self.evolution_algorithm.survive(population, offspring, population_size)
-                ind.fitnesses = self.evaluator.evaluate(ind.code)
-                print(f"新个体适应度：{ind.fitnesses}")
-            
-            # 合并父代和子代
-            combined_pop = population + offspring
-            
-            # 非支配排序和选择
-            population = self._select_new_population(combined_pop, population_size)
             
             # 保存当前代信息
             self.storage.save_generation(gen, population)
@@ -77,81 +94,6 @@ class MultiObjectiveEvolutionEngine:
         print("\n多目标进化完成！")
         return population
     
-    def _generate_offspring(self, population: List[MultiObjectiveIndividual]) -> List[MultiObjectiveIndividual]:
-        """生成子代个体"""
-        offspring = []
-        for _ in range(len(population) // 2):
-            # 选择父代
-            parents = self._select_parents(population)
-            
-            # 交叉 (这里可以集成LLM)
-            child_code = self._crossover(parents[0].code, parents[1].code)
-            
-            # 变异 (这里可以集成LLM)
-            mutated_code = self._mutate(child_code)
-            
-            offspring.append(MultiObjectiveIndividual(mutated_code, generation=parents[0].generation + 1))
-        return offspring
-    
-    def _select_parents(self, population: List[MultiObjectiveIndividual]) -> List[MultiObjectiveIndividual]:
-        """使用锦标赛选择父代"""
-        tournament_size = min(5, len(population))
-        parents = []
-        for _ in range(2):
-            candidates = np.random.choice(population, tournament_size, replace=False)
-            # 基于非支配排序和拥挤距离选择
-            candidates = sorted(candidates, key=lambda x: (
-                -x.rank,  # 优先选择前沿等级高的
-                x.crowding_distance(population)  # 其次选择拥挤距离大的
-            ))
-            parents.append(candidates[0])
-        return parents
-    
-    def _select_new_population(self, combined_pop: List[MultiObjectiveIndividual], size: int) -> List[MultiObjectiveIndividual]:
-        """选择新一代种群"""
-        fronts = self._non_dominated_sort(combined_pop)
-        new_pop = []
-        for front in fronts:
-            if len(new_pop) + len(front) <= 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[:size - len(new_pop)])
-                break
-        return new_pop
-    
-    def _non_dominated_sort(self, population: List[MultiObjectiveIndividual]) -> List[List[MultiObjectiveIndividual]]:
-        """非支配排序"""
-        fronts = [[]]
-        for ind in population:
-            ind.domination_count = 0
-            ind.dominated_set = []
-            for other in population:
-                if ind.dominates(other):
-                    ind.dominated_set.append(other)
-                elif other.dominates(ind):
-                    ind.domination_count += 1
-            if ind.domination_count == 0:
-                fronts[0].append(ind)
-                ind.rank = 0
-        
-        i = 0
-        while fronts[i]:
-            next_front = []
-            for ind in fronts[i]:
-                for dominated_ind in ind.dominated_set:
-                    dominated_ind.domination_count -= 1
-                    if dominated_ind.domination_count == 0:
-                        dominated_ind.rank = i + 1
-                        next_front.append(dominated_ind)
-            i += 1
-            fronts.append(next_front)
-        
-        return fronts[:-1]  # 最后一个是空的
-    
     def _calculate_pareto_front(self, population: List[MultiObjectiveIndividual]) -> List[MultiObjectiveIndividual]:
         """计算Pareto前沿"""
         front = []
@@ -165,16 +107,6 @@ class MultiObjectiveEvolutionEngine:
                 front.append(ind)
         return front
     
-    def _crossover(self, code1: str, code2: str) -> str:
-        """交叉操作"""
-        # 这里可以集成LLM的交叉操作
-        return code1  # 简化示例
-    
-    def _mutate(self, code: str) -> str:
-        """变异操作"""
-        # 这里可以集成LLM的变异操作
-        return code  # 简化示例
-    
     def _load_problem_config(self) -> dict:
         """加载问题配置"""
         config_path = os.path.join(self.problem_path, "problem_config.json")

src/lead/core/operators/crossover_analysis.py

@@ -14,14 +14,16 @@ class CrossoverAnalysisOperator:
                 "dominance": "parent1"|"parent2"|"non_dominated",
                 "target_objective": str,  # 主要优化目标
                 "parent1_strengths": List[str],  # parent1优势目标
-                "parent2_strengths": List[str]   # parent2优势目标
+                "parent2_strengths": List[str],  # parent2优势目标
+                "function_name": str      # 函数名
             }
         """
         result = {
             "dominance": self._check_dominance(parent1, parent2),
             "target_objective": None,
             "parent1_strengths": [],
-            "parent2_strengths": []
+            "parent2_strengths": [],
+            "function_name": self._extract_function_name(parent1.code)
         }
         
         # 比较各目标值，找出各自的优势目标
@@ -59,3 +61,10 @@ class CrossoverAnalysisOperator:
         elif ind2.dominates(ind1):
             return "parent2"
         return "non_dominated"
+        
+    def _extract_function_name(self, code: str) -> str:
+        """从代码中提取函数名"""
+        for line in code.split('\n'):
+            if line.strip().startswith('def '):
+                return line.split()[1].split('(')[0]
+        return "solution"  # 默认函数名

src/lead/core/operators/multi_objective_crossover.py

@@ -32,7 +32,7 @@ class MultiObjectiveCrossoverOperator:
         new_code = self.llm_client._call_llm(prompt, operator="crossover")
         
         # 4. 验证代码
-        verified_code = self._verify_code(new_code)
+        verified_code = self._verify_code(new_code, analysis_info["function_name"])
         
         return verified_code, analysis_info
     
@@ -57,6 +57,7 @@ class MultiObjectiveCrossoverOperator:
         1. 保留两个父代在各自优势目标上的优点
         2. 特别优化目标 '{target_obj}' 的性能
         3. 确保代码完整且符合问题要求
+        4. 函数名必须为: {analysis_info["function_name"]}
 
         只返回最终的Python代码，不要包含任何解释。
         """

src/lead/core/operators/multi_objective_mutation.py

@@ -24,6 +24,10 @@ class MultiObjectiveMutationOperator:
         analysis_info = self.analysis_operator.analyze(
             individual, objective_names, reference_front)
         
+        # 确保target_objective有效
+        if not analysis_info["target_objective"]:
+            analysis_info["target_objective"] = objective_names[0] if objective_names else "error"
+        
         # 2. 构建多目标变异提示词
         prompt = self._build_multi_obj_prompt(
             individual, analysis_info)
@@ -33,7 +37,7 @@ class MultiObjectiveMutationOperator:
         
         # 4. 验证代码
         verified_code = self._verify_code(
-            new_code, analysis_info["target_objective"])
+            new_code, analysis_info["function_name"])
         
         return verified_code, analysis_info
     
@@ -42,7 +46,7 @@ class MultiObjectiveMutationOperator:
                               analysis_info: Dict) -> str:
         """构建多目标变异提示词"""
         target_obj = analysis_info["target_objective"]
-        improvement = analysis_info["improvement_potential"][target_obj]
+        improvement = analysis_info["improvement_potential"].get(target_obj, 1.0)
         
         return f"""
         请对以下算法进行多目标优化变异，重点改进目标 '{target_obj}' (当前改进潜力: {improvement:.2f}):
@@ -55,6 +59,8 @@ class MultiObjectiveMutationOperator:
         2. 专门优化目标 '{target_obj}'
         3. 可以引入新的数学概念或优化方法
         4. 确保代码完整且可执行
+        5. 避免使用递归或限制递归深度
+        6. 函数名必须保持为: {analysis_info["function_name"]}
 
         只返回最终的Python代码，不要包含任何解释。
         """

src/lead/core/operators/mutation_analysis.py

@@ -13,30 +13,41 @@ class MutationAnalysisOperator:
             {
                 "target_objective": str,  # 主要优化目标
                 "improvement_potential": Dict[str, float],  # 各目标改进潜力
-                "nearest_solution": Dict[str, float]  # 参考前沿中最接近的解(可选)
+                "nearest_solution": Dict[str, float],  # 参考前沿中最接近的解(可选)
+                "function_name": str      # 函数名
             }
         """
         result = {
             "target_objective": None,
             "improvement_potential": {},
-            "nearest_solution": None
+            "nearest_solution": None,
+            "function_name": self._extract_function_name(individual.code)
         }
         
+        # 处理空fitnesses的情况
+        if not individual.fitnesses:
+            return result
+            
         # 计算各目标的改进潜力
         for obj in objective_names:
-            if reference_front:
+            if obj in individual.fitnesses:
-                # 如果有参考前沿，计算与前沿中最好解的差距
+                if reference_front:
-                best_val = min(sol[obj] for sol in reference_front)
+                    # 如果有参考前沿，计算与前沿中最好解的差距
-                result["improvement_potential"][obj] = individual.fitnesses[obj] - best_val
+                    best_val = min(sol[obj] for sol in reference_front)
-            else:
+                    result["improvement_potential"][obj] = individual.fitnesses[obj] - best_val
-                # 没有参考前沿，使用归一化值
+                else:
-                result["improvement_potential"][obj] = individual.fitnesses[obj]
+                    # 没有参考前沿，使用归一化值
+                    result["improvement_potential"][obj] = individual.fitnesses[obj]
         
         # 选择改进潜力最大的目标作为变异方向
-        result["target_objective"] = max(
+        if result["improvement_potential"]:
-            result["improvement_potential"].items(),
+            result["target_objective"] = max(
-            key=lambda x: x[1]
+                result["improvement_potential"].items(),
-        )[0]
+                key=lambda x: x[1]
+            )[0]
+        else:
+            # 如果没有有效的改进潜力数据，选择第一个目标
+            result["target_objective"] = objective_names[0] if objective_names else None
         
         # 如果有参考前沿，找到最接近的解
         if reference_front:
@@ -44,10 +55,17 @@ class MutationAnalysisOperator:
             for sol in reference_front:
                 distance = sum(
                     (individual.fitnesses[obj] - sol[obj]) ** 2
-                    for obj in objective_names
+                    for obj in objective_names if obj in individual.fitnesses
                 )
                 if distance < min_distance:
                     min_distance = distance
                     result["nearest_solution"] = sol
         
         return result
+        
+    def _extract_function_name(self, code: str) -> str:
+        """从代码中提取函数名"""
+        for line in code.split('\n'):
+            if line.strip().startswith('def '):
+                return line.split()[1].split('(')[0]
+        return "solution"  # 默认函数名

src/lead/problems/sort/evaluation.py

@@ -1,19 +1,27 @@
-from typing import Callable, List, Tuple
+from typing import Callable, List, Tuple, Dict
 import time
+import sys
 
-def evaluate(sort_func: Callable[[List[int]], List[int]], dataset: List[Tuple[List[int], List[int]]]) -> float:
+def evaluate(sort_func: Callable[[List[int]], List[int]], dataset: List[Tuple[List[int], List[int]]]) -> Dict[str, float]:
-    """评估排序算法的性能
+    """多目标评估排序算法的性能
     
     Args:
         sort_func: 排序函数
         dataset: [(输入数组, 正确排序结果), ...]
     
     Returns:
-        float: 适应度分数，分数越小表示性能越好
+        dict: 包含两个评估指标的字典，所有指标都是越小越好
+        {
+            "time": 平均执行时间(秒),
+            "error": 错误率(0-1)
+        }
     """
     total_time = 0
     success_count = 0
     
+    # 设置递归深度限制防止无限递归
+    sys.setrecursionlimit(1000)
+    
     for input_arr, expected in dataset:
         try:
             # 复制输入数组，避免修改原数组
@@ -30,15 +38,24 @@ def evaluate(sort_func: Callable[[List[int]], List[int]], dataset: List[Tuple[Li
                 total_time += execution_time
                 success_count += 1
             
+        except RecursionError:
+            print("递归深度超过限制，算法可能存在无限递归")
+            continue
         except Exception as e:
             print(f"测试用例执行失败: {str(e)}")
             continue
     
     if success_count == 0:
-        return float('inf')  # 如果全部失败返回无穷大
+        return {
+            "time": float('inf'),
+            "error": 1.0
+        }
     
-    # 计算最终适应度：平均执行时间 * (1 + 错误率惩罚)
+    # 计算各项指标
     avg_time = total_time / success_count
     error_rate = (len(dataset) - success_count) / len(dataset)
     
-    return avg_time * (1 + error_rate * 2)  # 错误率会增加最终得分
+    return {
+        "time": avg_time,
+        "error": error_rate
+    }

src/lead/problems/sort/multi_objective_evolution.log

@@ -0,0 +1,2 @@
+Generation 0000 | Pareto Front Size: 2 | Timestamp: 2025-04-15T18:08:58.280070
+Generation 0001 | Pareto Front Size: 2 | Timestamp: 2025-04-15T18:10:29.667065

src/lead/problems/sort/multi_objective_history/20250415_180647/generation_0.json

@@ -0,0 +1,67 @@
+{
+  "generation": 0,
+  "population": [
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n    \n    def quicksort(arr, low, high):\n        if low < high:\n            pi = partition(arr, low, high)\n            quicksort(arr, low, pi - 1)\n            quicksort(arr, pi + 1, high)\n\n    def partition(arr, low, high):\n        pivot = arr[(low + high) // 2]\n        i = low - 1\n        j = high + 1\n\n        while True:\n            i += 1\n            while arr[i] < pivot:\n                i += 1\n            j -= 1\n            while arr[j] > pivot:\n                j -= 1\n            \n            if i >= j:\n                return j\n            \n            arr[i], arr[j] = arr[j], arr[i]\n\n    def insertion_sort(arr):\n        for i in range(1, len(arr)):\n            key = arr[i]\n            j = i - 1\n            while j >= 0 and key < arr[j]:\n                arr[j + 1] = arr[j]\n                j -= 1\n            arr[j + 1] = key\n\n    if len(arr) <= 10:\n        insertion_sort(arr)\n    else:\n        quicksort(arr, 0, len(arr) - 1)\n\n    return arr",
+      "generation": 0,
+      "fitnesses": {
+        "time": 0.07625889778137207,
+        "error": 0.75
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n\n    def insertion_sort(sub_arr):\n        for i in range(1, len(sub_arr)):\n            key = sub_arr[i]\n            j = i - 1\n            while j >= 0 and sub_arr[j] > key:\n                sub_arr[j + 1] = sub_arr[j]\n                j -= 1\n            sub_arr[j + 1] = key\n        return sub_arr\n\n    def quicksort(sub_arr):\n        if len(sub_arr) <= 10:\n            return insertion_sort(sub_arr)\n        else:\n            pivot = sub_arr[len(sub_arr) // 2]\n            left = [x for x in sub_arr if x < pivot]\n            middle = [x for x in sub_arr if x == pivot]\n            right = [x for x in sub_arr if x > pivot]\n            return quicksort(left) + middle + quicksort(right)\n\n    return quicksort(arr)",
+      "generation": 1,
+      "fitnesses": {
+        "time": 0.08427751064300537,
+        "error": 0.0
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n\n    def insertion_sort(sub_arr):\n        for i in range(1, len(sub_arr)):\n            key = sub_arr[i]\n            j = i - 1\n            while j >= 0 and sub_arr[j] > key:\n                sub_arr[j + 1] = sub_arr[j]\n                j -= 1\n            sub_arr[j + 1] = key\n        return sub_arr\n\n    def quicksort(arr, low, high):\n        if low < high:\n            pi = partition(arr, low, high)\n            quicksort(arr, low, pi)\n            quicksort(arr, pi + 1, high)\n\n    def partition(arr, low, high):\n        pivot = arr[(low + high) // 2]\n        i = low - 1\n        j = high + 1\n\n        while True:\n            i += 1\n            while arr[i] < pivot:\n                i += 1\n            j -= 1\n            while arr[j] > pivot:\n                j -= 1\n            \n            if i >= j:\n                return j\n            \n            arr[i], arr[j] = arr[j], arr[i]\n\n    if len(arr) <= 10:\n        return insertion_sort(arr)\n    else:\n        quicksort(arr, 0, len(arr) - 1)\n\n    return arr",
+      "generation": 1,
+      "fitnesses": {
+        "time": 0.1091681718826294,
+        "error": 0.0
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n\n    def insertion_sort(sub_arr):\n        for i in range(1, len(sub_arr)):\n            key = sub_arr[i]\n            j = i - 1\n            while j >= 0 and sub_arr[j] > key:\n                sub_arr[j + 1] = sub_arr[j]\n                j -= 1\n            sub_arr[j + 1] = key\n        return sub_arr\n\n    def quicksort(sub_arr):\n        if len(sub_arr) <= 10:\n            return insertion_sort(sub_arr)\n        else:\n            pivot = sub_arr[len(sub_arr) // 2]\n            left = [x for x in sub_arr if x < pivot]\n            middle = [x for x in sub_arr if x == pivot]\n            right = [x for x in sub_arr if x > pivot]\n            return quicksort(left) + middle + quicksort(right)\n\n    return quicksort(arr)",
+      "generation": 0,
+      "fitnesses": {
+        "time": 0.17589008808135986,
+        "error": 0.0
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n\n    def counting_sort(arr):\n        max_val = max(arr)\n        min_val = min(arr)\n        range_of_elements = max_val - min_val + 1\n        count = [0] * range_of_elements\n        output = [0] * len(arr)\n\n        for num in arr:\n            count[num - min_val] += 1\n\n        for i in range(1, len(count)):\n            count[i] += count[i - 1]\n\n        for num in reversed(arr):\n            output[count[num - min_val] - 1] = num\n            count[num - min_val] -= 1\n\n        return output\n\n    def quick_sort(arr):\n        if len(arr) < 2:\n            return arr\n\n        pivot = arr[len(arr) // 2]\n        left = [x for x in arr if x < pivot]\n        middle = [x for x in arr if x == pivot]\n        right = [x for x in arr if x > pivot]\n\n        return quick_sort(left) + middle + quick_sort(right)\n\n    unique_elements = set(arr)\n    \n    if len(unique_elements) * 2 < len(arr):\n        return counting_sort(arr)\n    else:\n        return quick_sort(arr)",
+      "generation": 0,
+      "fitnesses": {
+        "time": 0.2675272822380066,
+        "error": 0.0
+      }
+    }
+  ],
+  "pareto_front": [
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n    \n    def quicksort(arr, low, high):\n        if low < high:\n            pi = partition(arr, low, high)\n            quicksort(arr, low, pi - 1)\n            quicksort(arr, pi + 1, high)\n\n    def partition(arr, low, high):\n        pivot = arr[(low + high) // 2]\n        i = low - 1\n        j = high + 1\n\n        while True:\n            i += 1\n            while arr[i] < pivot:\n                i += 1\n            j -= 1\n            while arr[j] > pivot:\n                j -= 1\n            \n            if i >= j:\n                return j\n            \n            arr[i], arr[j] = arr[j], arr[i]\n\n    def insertion_sort(arr):\n        for i in range(1, len(arr)):\n            key = arr[i]\n            j = i - 1\n            while j >= 0 and key < arr[j]:\n                arr[j + 1] = arr[j]\n                j -= 1\n            arr[j + 1] = key\n\n    if len(arr) <= 10:\n        insertion_sort(arr)\n    else:\n        quicksort(arr, 0, len(arr) - 1)\n\n    return arr",
+      "generation": 0,
+      "fitnesses": {
+        "time": 0.07625889778137207,
+        "error": 0.75
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n\n    def insertion_sort(sub_arr):\n        for i in range(1, len(sub_arr)):\n            key = sub_arr[i]\n            j = i - 1\n            while j >= 0 and sub_arr[j] > key:\n                sub_arr[j + 1] = sub_arr[j]\n                j -= 1\n            sub_arr[j + 1] = key\n        return sub_arr\n\n    def quicksort(sub_arr):\n        if len(sub_arr) <= 10:\n            return insertion_sort(sub_arr)\n        else:\n            pivot = sub_arr[len(sub_arr) // 2]\n            left = [x for x in sub_arr if x < pivot]\n            middle = [x for x in sub_arr if x == pivot]\n            right = [x for x in sub_arr if x > pivot]\n            return quicksort(left) + middle + quicksort(right)\n\n    return quicksort(arr)",
+      "generation": 1,
+      "fitnesses": {
+        "time": 0.08427751064300537,
+        "error": 0.0
+      }
+    }
+  ],
+  "objective_names": [
+    "time",
+    "error"
+  ]
+}

src/lead/problems/sort/multi_objective_history/20250415_180647/generation_1.json

@@ -0,0 +1,67 @@
+{
+  "generation": 1,
+  "population": [
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n    \n    def quicksort(arr, low, high):\n        if low < high:\n            pi = partition(arr, low, high)\n            quicksort(arr, low, pi - 1)\n            quicksort(arr, pi + 1, high)\n\n    def partition(arr, low, high):\n        pivot = arr[(low + high) // 2]\n        i = low - 1\n        j = high + 1\n\n        while True:\n            i += 1\n            while arr[i] < pivot:\n                i += 1\n            j -= 1\n            while arr[j] > pivot:\n                j -= 1\n            \n            if i >= j:\n                return j\n            \n            arr[i], arr[j] = arr[j], arr[i]\n\n    def insertion_sort(arr):\n        for i in range(1, len(arr)):\n            key = arr[i]\n            j = i - 1\n            while j >= 0 and key < arr[j]:\n                arr[j + 1] = arr[j]\n                j -= 1\n            arr[j + 1] = key\n\n    if len(arr) <= 10:\n        insertion_sort(arr)\n    else:\n        quicksort(arr, 0, len(arr) - 1)\n\n    return arr",
+      "generation": 0,
+      "fitnesses": {
+        "time": 0.07625889778137207,
+        "error": 0.75
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n\n    def insertion_sort(sub_arr):\n        for i in range(1, len(sub_arr)):\n            key = sub_arr[i]\n            j = i - 1\n            while j >= 0 and sub_arr[j] > key:\n                sub_arr[j + 1] = sub_arr[j]\n                j -= 1\n            sub_arr[j + 1] = key\n        return sub_arr\n\n    def quicksort(sub_arr):\n        if len(sub_arr) <= 10:\n            return insertion_sort(sub_arr)\n        else:\n            pivot = sub_arr[len(sub_arr) // 2]\n            left = [x for x in sub_arr if x < pivot]\n            middle = [x for x in sub_arr if x == pivot]\n            right = [x for x in sub_arr if x > pivot]\n            return quicksort(left) + middle + quicksort(right)\n\n    return quicksort(arr)",
+      "generation": 1,
+      "fitnesses": {
+        "time": 0.08427751064300537,
+        "error": 0.0
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n\n    def insertion_sort(sub_arr):\n        for i in range(1, len(sub_arr)):\n            key = sub_arr[i]\n            j = i - 1\n            while j >= 0 and sub_arr[j] > key:\n                sub_arr[j + 1] = sub_arr[j]\n                j -= 1\n            sub_arr[j + 1] = key\n        return sub_arr\n\n    def quicksort(sub_arr):\n        if len(sub_arr) <= 10:\n            return insertion_sort(sub_arr)\n        else:\n            pivot = sub_arr[len(sub_arr) // 2]\n            left = [x for x in sub_arr if x < pivot]\n            middle = [x for x in sub_arr if x == pivot]\n            right = [x for x in sub_arr if x > pivot]\n            return quicksort(left) + middle + quicksort(right)\n\n    return quicksort(arr)",
+      "generation": 0,
+      "fitnesses": {
+        "time": 0.17589008808135986,
+        "error": 0.0
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n\n    def insertion_sort(sub_arr):\n        for i in range(1, len(sub_arr)):\n            key = sub_arr[i]\n            j = i - 1\n            while j >= 0 and sub_arr[j] > key:\n                sub_arr[j + 1] = sub_arr[j]\n                j -= 1\n            sub_arr[j + 1] = key\n        return sub_arr\n\n    def quicksort(arr, low, high):\n        if low < high:\n            pi = partition(arr, low, high)\n            quicksort(arr, low, pi)\n            quicksort(arr, pi + 1, high)\n\n    def partition(arr, low, high):\n        pivot = arr[(low + high) // 2]\n        i = low - 1\n        j = high + 1\n\n        while True:\n            i += 1\n            while arr[i] < pivot:\n                i += 1\n            j -= 1\n            while arr[j] > pivot:\n                j -= 1\n            \n            if i >= j:\n                return j\n            \n            arr[i], arr[j] = arr[j], arr[i]\n\n    if len(arr) <= 10:\n        return insertion_sort(arr)\n    else:\n        quicksort(arr, 0, len(arr) - 1)\n\n    return arr",
+      "generation": 1,
+      "fitnesses": {
+        "time": 0.3470318913459778,
+        "error": 0.0
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n    \n    def quicksort(arr, low, high):\n        if low < high:\n            pi = partition(arr, low, high)\n            quicksort(arr, low, pi - 1)\n            quicksort(arr, pi + 1, high)\n\n    def partition(arr, low, high):\n        pivot = arr[(low + high) // 2]\n        i = low - 1\n        j = high + 1\n\n        while True:\n            i += 1\n            while i < high and arr[i] < pivot:\n                i += 1\n            j -= 1\n            while j > low and arr[j] > pivot:\n                j -= 1\n            \n            if i >= j:\n                return j\n            \n            arr[i], arr[j] = arr[j], arr[i]\n\n    def insertion_sort(arr):\n        for i in range(1, len(arr)):\n            key = arr[i]\n            j = i - 1\n            while j >= 0 and key < arr[j]:\n                arr[j + 1] = arr[j]\n                j -= 1\n            arr[j + 1] = key\n\n    if len(arr) <= 10:\n        insertion_sort(arr)\n    else:\n        quicksort(arr, 0, len(arr) - 1)\n\n    return arr",
+      "generation": 1,
+      "fitnesses": {
+        "time": 0.24364137649536133,
+        "error": 0.75
+      }
+    }
+  ],
+  "pareto_front": [
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n    \n    def quicksort(arr, low, high):\n        if low < high:\n            pi = partition(arr, low, high)\n            quicksort(arr, low, pi - 1)\n            quicksort(arr, pi + 1, high)\n\n    def partition(arr, low, high):\n        pivot = arr[(low + high) // 2]\n        i = low - 1\n        j = high + 1\n\n        while True:\n            i += 1\n            while arr[i] < pivot:\n                i += 1\n            j -= 1\n            while arr[j] > pivot:\n                j -= 1\n            \n            if i >= j:\n                return j\n            \n            arr[i], arr[j] = arr[j], arr[i]\n\n    def insertion_sort(arr):\n        for i in range(1, len(arr)):\n            key = arr[i]\n            j = i - 1\n            while j >= 0 and key < arr[j]:\n                arr[j + 1] = arr[j]\n                j -= 1\n            arr[j + 1] = key\n\n    if len(arr) <= 10:\n        insertion_sort(arr)\n    else:\n        quicksort(arr, 0, len(arr) - 1)\n\n    return arr",
+      "generation": 0,
+      "fitnesses": {
+        "time": 0.07625889778137207,
+        "error": 0.75
+      }
+    },
+    {
+      "code": "def advanced_sort(arr):\n    if len(arr) < 2:\n        return arr\n\n    def insertion_sort(sub_arr):\n        for i in range(1, len(sub_arr)):\n            key = sub_arr[i]\n            j = i - 1\n            while j >= 0 and sub_arr[j] > key:\n                sub_arr[j + 1] = sub_arr[j]\n                j -= 1\n            sub_arr[j + 1] = key\n        return sub_arr\n\n    def quicksort(sub_arr):\n        if len(sub_arr) <= 10:\n            return insertion_sort(sub_arr)\n        else:\n            pivot = sub_arr[len(sub_arr) // 2]\n            left = [x for x in sub_arr if x < pivot]\n            middle = [x for x in sub_arr if x == pivot]\n            right = [x for x in sub_arr if x > pivot]\n            return quicksort(left) + middle + quicksort(right)\n\n    return quicksort(arr)",
+      "generation": 1,
+      "fitnesses": {
+        "time": 0.08427751064300537,
+        "error": 0.0
+      }
+    }
+  ],
+  "objective_names": [
+    "time",
+    "error"
+  ]
+}

src/lead/problems/sort/problem_config.json

@@ -1,23 +1,24 @@
 {
-    "description": "实现一个高效的排序算法，要求时间复杂度尽可能低。算法需要处理各种情况：随机数组、接近有序的数组、逆序数组、包含大量重复元素的数组等。同时由于你拥有的评估时间仅有2秒，请确保你的算法在2秒内能够完成排序。而且你只能使用python语言。注意我们用于评估你的测试用例的数组大小为100000，请确保你的算法在100000个元素的数组上也能在2秒内完成排序。",
+    "description": "实现一个高效的排序算法，需要同时优化执行时间和正确性。算法需要处理各种情况：随机数组、接近有序的数组、逆序数组、包含大量重复元素的数组等。",
     "function_name": "advanced_sort",
     "input_format": "一个整数列表arr，包含需要排序的数字",
     "output_format": "返回排序后的列表，按照从小到大的顺序排列",
     "evaluation_timeout": 2,
+    "multi_objective": true,
+    "objective_names": ["time", "error"],
     "llm_config": {
         "api_key": "sk-5wHuJRuC2KDMINeYERru0zCCMrRTzmkrOMwpvBQr4EULV0Tv",
         "base_url": "https://xiaohumini.site/v1/chat/completions",
-        "model": "gemini-1.5-flash-latest",
+        "model": "gpt-4o-mini",
         "temperature": 0.5,
         "max_tokens": 4096
     },
     "evolution_params": {
-        "algorithm": "TU",
+        "algorithm": "MO",
         "population_size": 5,
         "generations": 2,
         "mutation_rate": 0.3,
-        "crossover_rate": 0.5,
+        "crossover_rate": 0.7,
-        "F": 0.2,
+        "tournament_size": 3
-        "CR": 0.5
     }
 }

src/lead/scripts/run_evolution.py

@@ -1,7 +1,9 @@
 import os
 import sys
 import argparse
+import json
 from lead.core.evolution import EvolutionEngine
+from lead.core.multi_objective.evolution import MultiObjectiveEvolutionEngine
 from lead.config.settings import PROBLEMS_DIR
 
 def parse_args():
@@ -14,6 +16,19 @@ def parse_args():
                       help="种群大小")
     return parser.parse_args()
 
+def load_engine_config(problem_path):
+    """加载问题配置并决定使用哪种引擎"""
+    config_path = os.path.join(problem_path, "problem_config.json")
+    with open(config_path, "r", encoding="utf-8") as f:
+        config = json.load(f)
+    
+    if config.get("multi_objective", False):
+        print("使用多目标进化引擎")
+        return MultiObjectiveEvolutionEngine(problem_path)
+    else:
+        print("使用单目标进化引擎")
+        return EvolutionEngine(problem_path)
+
 def main():
     args = parse_args()
     problem_path = os.path.join(PROBLEMS_DIR, args.problem)
@@ -27,7 +42,7 @@ def main():
     print(f"开始进化求解问题: {args.problem}")
     print(f"配置: 代数={args.generations or '默认'}，种群大小={args.population or '默认'}")
     
-    engine = EvolutionEngine(problem_path)
+    engine = load_engine_config(problem_path)
     engine.run_evolution(
         generations=args.generations,
         population_size=args.population