遗传算法相关参数设置的Python代码
时间: 2023-11-21 15:57:38 浏览: 26
以下是一个简单的遗传算法的Python代码,包括相关参数设置:
```
import random
# 遗传算法相关参数
POPULATION_SIZE = 50 # 种群规模
GENERATION_NUMBER = 100 # 迭代次数
MUTATION_PROBABILITY = 0.05 # 变异概率
ELITE_NUMBER = 10 # 精英个体数量
# 个体类
class Individual:
def __init__(self, chromosome):
self.chromosome = chromosome
self.fitness = self.calculate_fitness()
# 计算适应度函数
def calculate_fitness(self):
pass
# 变异
def mutate(self):
pass
# 初始化种群
def initialize_population():
population = []
for i in range(POPULATION_SIZE):
chromosome = [random.randint(0, 1) for j in range(10)]
population.append(Individual(chromosome))
return population
# 选择
def select(population):
pass
# 交叉
def crossover(parent1, parent2):
pass
# 进化
def evolve(population):
elite = sorted(population, key=lambda x: x.fitness, reverse=True)[:ELITE_NUMBER]
parents = select(population)
offspring = []
while len(offspring) < POPULATION_SIZE - ELITE_NUMBER:
parent1, parent2 = random.sample(parents, 2)
child1, child2 = crossover(parent1, parent2)
child1.mutate()
child2.mutate()
offspring.extend([child1, child2])
population = elite + offspring
return population
# 主程序入口
def main():
population = initialize_population()
for i in range(GENERATION_NUMBER):
population = evolve(population)
best_individual = max(population, key=lambda x: x.fitness)
print("Best individual:", best_individual.chromosome)
print("Best fitness:", best_individual.fitness)
if __name__ == "__main__":
main()
```
注意,这只是一个简单的遗传算法示例,适应度函数、选择、交叉以及变异函数需要根据具体问题进行设计和实现。