java - Java问题中的遗传算法

Question

我在用 Java 创建遗传算法时遇到问题。我正在参加在线 GA 竞赛。我每次都试图将最好的结果保存回索引 0，但它只是成为对原始索引的引用。这意味着当我发展其余索引时，如果它发展出最好的成员原始索引，我就会失去它。

我尝试用 getClone 方法填充它，该方法将对象数据转换为 int 数组并从中创建一个新对象。

个人类:

class Individual {
    public int[] angle;
    public int[] thrust;
    public double fitness;
    public Individual(){
        angle = new int[2];
        thrust = new int[2];
        for (int i = 0; i < 2; i++) {    
            this.angle[i] = ThreadLocalRandom.current().nextInt(0, 37) - 18;
            this.thrust[i] = ThreadLocalRandom.current().nextInt(0, 202);
            this.thrust[i] = ( (this.thrust[i] == 201) ? 650 : this.thrust[i] );
        }
        this.fitness = Double.MIN_VALUE;
    }

    public Individual(int[][] genes, double f){
        this.fitness = f;
        angle = new int[2];
        thrust = new int[2];
        this.angle[0]  = genes[0][0];
        this.angle[1]  = genes[0][1];
        this.thrust[0] = genes[1][0];
        this.thrust[1] = genes[1][1];    
    }

    public Individual getClone() {
        int[][] genes = new int[2][2];
        genes[0][0] = (int)this.angle[0];
        genes[0][1] = (int)this.angle[1];
        genes[1][0] = (int)this.thrust[0];
        genes[1][1] = (int)this.thrust[1];
        return ( new Individual(genes, this.fitness) );
    }

    public Individual crossover(Individual other) {
        int[][] genes = new int[2][2];
        genes[0][0] = (int)( (this.angle[0] + other.angle[0])/2 );
        genes[0][1] = (int)( (this.angle[1] + other.angle[1])/2 );
        genes[1][0] = ( (this.thrust[0] == 650 || other.thrust[0] == 650) ? 650: (int)( (this.thrust[0] + other.thrust[0])/2 ) );
        genes[1][1] = ( (this.thrust[1] == 650 || other.thrust[1] == 650) ? 650: (int)( (this.thrust[1] + other.thrust[1])/2 ) );
        return ( new Individual(genes, Double.MIN_VALUE) );
    }

    public void mutate() {
        for (int i = 0; i < 2; i++) {
            if(ThreadLocalRandom.current().nextInt(0, 2)==1) { 
                this.angle[i] = ThreadLocalRandom.current().nextInt(0, 37) - 18;
            }
            if(ThreadLocalRandom.current().nextInt(0, 2)==1) {
                this.thrust[i] = ThreadLocalRandom.current().nextInt(0, 202);
                this.thrust[i] = ( (this.thrust[i] == 201) ? 650 : this.thrust[i] );
            }
        }
    }

人口类别:

class Population {
    public Individual[] individuals;

    public Population(int populationSize) {
        individuals = new Individual[populationSize];
        for (int i = 0; i < populationSize; i ++) {
            individuals[i] = new Individual();
        }
    }

    public void resetFitness() {
        for (int i = 0; i < individuals.length; i++) {
            individuals[i].fitness = Double.MIN_VALUE;
        }
    }
    public void setIndividual(int i, Individual indiv) {
        individuals[i] = indiv.getClone();
    }

    public Individual getIndividual(int i) {
        return individuals[i].getClone();
    }

    public int size() { 
        return this.individuals.length;    
    }
    public Individual getFittest() {
        int fittest = 0;
        // Loop through individuals to find fittest
        for (int i = 0; i < individuals.length; i++) {
            if (individuals[i].fitness > individuals[fittest].fitness) {
                fittest = i;
            }
        }
        return individuals[fittest].getClone();
    }
}

模拟类的必需品:

class simGA {
    private Population pop; 
    private final static int TSIZE = 5; //tournement size

    public simGA (int poolsize) { 
        this.pop = new Population(poolsize);
    }

    public Individual search(int generations, int totalMoves) {
        //this.pop.resetFitness();
        for (int g = 0; g < generations; g++) {
            for (int i = 0; i < this.pop.individuals.length; i++) {
                this.pop.individuals[i].fitness = sim(this.pop.individuals[i],totalMoves);
            }
            System.err.print("Generation " + g + " ");
            this.pop = evolvePopulation(this.pop);
        }
        return pop.getFittest();
    }

    private Population evolvePopulation(Population p) {
        //save fittest
        Population tempPop = new Population(p.individuals.length);
        tempPop.setIndividual(0, p.getFittest().getClone() );
        System.err.print("Best move: " + tempPop.individuals[0].fitness);
        System.err.println();
        for (int i = 1; i < p.individuals.length; i++) {
            Individual indiv1 = tournamentSelection(p);
            Individual indiv2 = tournamentSelection(p);
            Individual newIndiv = indiv1.crossover(indiv2);
            newIndiv.mutate();
            tempPop.setIndividual(i, newIndiv.getClone() );
        }
        return tempPop;
    }

    // Select individuals for crossover
    private Individual tournamentSelection(Population pop) {
        // Create a tournament population
        Population tournament = new Population(TSIZE);
        // For each place in the tournament get a random individual
        for (int i = 0; i < TSIZE; i++) {
            int randomId = ThreadLocalRandom.current().nextInt(1, this.pop.individuals.length);
            tournament.setIndividual(i, pop.getIndividual(randomId).getClone() );
        }
        // Get the fittest
        return tournament.getFittest().getClone();
    }

    private double sim(Individual s, int moves) {
        return score; //score of simmed moves
    }

我如何确保最优秀的人得到保存，而不是作为引用？当我错误地打印出最好的分数时，有时它会丢失并且选择了更差的得分 Action 。我不认为这一定是对象克隆问题，我可以很好地克隆模拟的游戏对象，每次运行都重置它们。

正如我所说，这是为了比赛，所以我不能使用网站上的任何库，这也是我没有发布完整代码的原因，模拟器本身对 Action 进行评分的复杂性不被赠送。但只要说在纸上计算出来的分数符合预期就足够了。

我回复 NWS，我认为我的 getClone 方法正在执行深拷贝。

除 wiki 和其他遗传算法知识外使用的引用资料:http://www.theprojectspot.com/tutorial-post/creating-a-genetic-algorithm-for-beginners/3

我已经通过不重新调整索引 0 处的个人来修复它。但这意味着我的代码存在与问题无关的其他问题。

Answer 1

Individual newIndiv = indiv1.crossover(indiv2);

上一行将适应度重置为 Double.MIN_VALUE。因此，每当调用 evolvePopulation 时，只有索引 0 处的个体是最适者。