遗传算法代码，不调用其他库

好的，以下是遗传算法的简单 Python 代码，不调用任何其他库：

import random

def create_individual(length, geneSet, get_fitness):
    genes = []
    while len(genes) < length:
        sampleSize = min(length - len(genes), len(geneSet))
        genes.extend(random.sample(geneSet, sampleSize))
    fitness = get_fitness(genes)
    return Chromosome(genes, fitness)

def mutate(parent, geneSet, get_fitness):
    index = random.randrange(0, len(parent.Genes))
    childGenes = parent.Genes[:]
    newGene, alternate = random.sample(geneSet, 2)
    childGenes[index] = alternate if newGene == childGenes[index] else newGene
    fitness = get_fitness(childGenes)
    return Chromosome(childGenes, fitness)

def crossover(parentGenes, index, offspring_length, geneSet, get_fitness):
    genes1 = parentGenes[0][:index] + parentGenes[1][index:]
    genes2 = parentGenes[1][:index] + parentGenes[0][index:]
    fitness1 = get_fitness(genes1)
    fitness2 = get_fitness(genes2)
    return Chromosome(genes1, fitness1), Chromosome(genes2, fitness2)

class Chromosome:
    def __init__(self, genes, fitness):
        self.Genes = genes
        self.Fitness = fitness

def get_best(get_fitness, targetLen, optimalFitness, geneSet, display):
    random.seed()
    bestParent = create_individual(targetLen, geneSet, get_fitness)
    display(bestParent)
    if bestParent.Fitness >= optimalFitness:
        return bestParent
    while True:
        child = mutate(bestParent, geneSet, get_fitness)
        if bestParent.Fitness >= child.Fitness:
            continue
        display(child)
        if child.Fitness >= optimalFitness:
            return child
        bestParent = child

这份代码实现了遗传算法的基本步骤，包括生成初始种群，变异，交叉，选择最优解等。希望这份代码能够帮助您理解遗传算法的工作原理。