python写遗传算法

以下是一个Python遗传算法的示例，用于解决最大化函数f(x) = x*sin(10*pi*x)+2.0（其中0<=x<=1）。该算法具有以下参数：

- popSize：种群数量
- eliteSize：精英数量
- mutationRate：突变率
- generations：遗传算法的迭代次数

代码如下：

import random
import math

def fitness(x):
    return x * math.sin(10 * math.pi * x) + 2.0

def generateIndividual():
    return random.random()

def generatePopulation(popSize):
    return [generateIndividual() for i in range(popSize)]

def rankPopulation(population):
    fitnessResults = {}
    for i in range(len(population)):
        fitnessResults[i] = fitness(population[i])
    return sorted(fitnessResults.items(), key = lambda x : x[1], reverse = True)

def selectParents(populationRanked, eliteSize):
    parents = []
    for i in range(eliteSize):
        parents.append(populationRanked[i][0])
    for i in range(len(populationRanked) - eliteSize):
        if random.random() < 0.5:
            parents.append(populationRanked[i][0])
    return parents

def breed(parent1, parent2):
    child = []
    geneA = int(random.random() * len(parent1))
    geneB = int(random.random() * len(parent1))
    startGene = min(geneA, geneB)
    endGene = max(geneA, geneB)
    for i in range(startGene, endGene):
        child.append(parent1[i])
    for i in range(len(parent2)):
        if parent2[i] not in child:
            child.append(parent2[i])
    return child

def breedPopulation(parents, eliteSize):
    children = []
    length = len(parents) - eliteSize
    pool = random.sample(parents, len(parents))
    for i in range(eliteSize):
        children.append(parents[i])
    for i in range(length):
        child = breed(pool[i], pool[len(parents) - i - 1])
        children.append(child)
    return children

def mutate(individual, mutationRate):
    if random.random() < mutationRate:
        index = int(random.random() * len(individual))
        individual[index] = random.random()
    return individual

def mutatePopulation(population, mutationRate):
    mutatedPopulation = []
    for individual in population:
        mutatedIndividual = mutate(individual, mutationRate)
        mutatedPopulation.append(mutatedIndividual)
    return mutatedPopulation

def nextGeneration(currentGen, eliteSize, mutationRate):
    populationRanked = rankPopulation(currentGen)
    parents = selectParents(populationRanked, eliteSize)
    children = breedPopulation(parents, eliteSize)
    nextGeneration = mutatePopulation(children, mutationRate)
    return nextGeneration

def geneticAlgorithm(popSize, eliteSize, mutationRate, generations):
    population = generatePopulation(popSize)
    for i in range(generations):
        population = nextGeneration(population, eliteSize, mutationRate)
    populationRanked = rankPopulation(population)
    return populationRanked[0][0]

print(geneticAlgorithm(popSize = 100, eliteSize = 20, mutationRate = 0.01, generations = 100)) 

这个示例代码使用了Python的随机数生成器和数学库，以便随机生成个体和进行适应度计算。在主函数中，我们使用100个种群，精英数量为20，突变率为0.01，进行100代的遗传算法迭代。输出的结果是一个最优解，即f(x)的最大值对应的x值。