用Java写个遗传算法基础模型

好的，以下是一个用Java编写的遗传算法基础模型：

import java.util.Random;

public class GeneticAlgorithm {
    
    // 种群数量
    private int populationSize;
    
    // 基因序列长度
    private int geneLength;
    
    // 目标基因序列
    private int[] targetGene;
    
    // 种群
    private int[][] population;
    
    // 种群中每个个体的适应度
    private int[] fitness;
    
    // 随机数生成器
    private Random random;
    
    public GeneticAlgorithm(int populationSize, int geneLength, int[] targetGene) {
        this.populationSize = populationSize;
        this.geneLength = geneLength;
        this.targetGene = targetGene;
        this.population = new int[populationSize][geneLength];
        this.fitness = new int[populationSize];
        this.random = new Random();
        initializePopulation();
    }
    
    // 初始化种群
    private void initializePopulation() {
        for (int i = 0; i < populationSize; i++) {
            for (int j = 0; j < geneLength; j++) {
                population[i][j] = random.nextInt(2);
            }
        }
    }
    
    // 计算种群中每个个体的适应度
    private void calculateFitness() {
        for (int i = 0; i < populationSize; i++) {
            int score = 0;
            for (int j = 0; j < geneLength; j++) {
                if (population[i][j] == targetGene[j]) {
                    score++;
                }
            }
            fitness[i] = score;
        }
    }
    
    // 选择个体进行交叉
    private int[] selectParent() {
        int[][] selectedParents = new int[2][geneLength];
        for (int i = 0; i < 2; i++) {
            int index1 = random.nextInt(populationSize);
            int index2 = random.nextInt(populationSize);
            selectedParents[i] = fitness[index1] > fitness[index2] ? population[index1] : population[index2];
        }
        return selectedParents[0];
    }
    
    // 交叉
    private int[] crossover(int[] parent1, int[] parent2) {
        int crossoverPoint = random.nextInt(geneLength);
        int[] child = new int[geneLength];
        for (int i = 0; i < geneLength; i++) {
            if (i < crossoverPoint) {
                child[i] = parent1[i];
            } else {
                child[i] = parent2[i];
            }
        }
        return child;
    }
    
    // 变异
    private void mutate(int[] child) {
        for (int i = 0; i < geneLength; i++) {
            if (random.nextDouble() < 0.01) {
                child[i] = child[i] == 0 ? 1 : 0;
            }
        }
    }
    
    // 进化
    public int[] evolve() {
        calculateFitness();
        int[] parent1 = selectParent();
        int[] parent2 = selectParent();
        int[] child = crossover(parent1, parent2);
        mutate(child);
        return child;
    }
}

这是一个简单的遗传算法实现，可以使用它来求解基因序列匹配问题。您可以根据自