遗传算法自动组卷的Java代码

下面是遗传算法自动组卷的Java代码示例：

import java.util.ArrayList;
import java.util.Random;

public class GeneticAlgorithm {
    private int populationSize;
    private double mutationRate;
    private double crossoverRate;
    private int elitismCount;

    public GeneticAlgorithm(int populationSize, double mutationRate, double crossoverRate, int elitismCount) {
        this.populationSize = populationSize;
        this.mutationRate = mutationRate;
        this.crossoverRate = crossoverRate;
        this.elitismCount = elitismCount;
    }

    public ArrayList<Solution> evolvePopulation(ArrayList<Solution> population) {
        ArrayList<Solution> newPopulation = new ArrayList<>();

        // Keep elites
        for (int i = 0; i < this.elitismCount; i++) {
            newPopulation.add(population.get(i));
        }

        // Crossover
        while (newPopulation.size() < this.populationSize) {
            Solution parent1 = selectPopulation(population);
            Solution parent2 = selectPopulation(population);
            Solution child = crossover(parent1, parent2);
            newPopulation.add(child);
        }

        // Mutate
        for (int i = this.elitismCount; i < newPopulation.size(); i++) {
            mutate(newPopulation.get(i));
        }

        return newPopulation;
    }

    private Solution selectPopulation(ArrayList<Solution> population) {
        int index = new Random().nextInt(population.size());
        return population.get(index);
    }

    private Solution crossover(Solution parent1, Solution parent2) {
        Solution child = new Solution();
        int crossoverPoint = new Random().nextInt(parent1.getGenes().size());
        for (int i = 0; i < parent1.getGenes().size(); i++) {
            if (i < crossoverPoint) {
                child.getGenes().add(parent1.getGenes().get(i));
            } else {
                child.getGenes().add(parent2.getGenes().get(i));
            }
        }
        return child;
    }

    private void mutate(Solution solution) {
        for (int i = 0; i < solution.getGenes().size(); i++) {
            if (new Random().nextDouble() < this.mutationRate) {
                solution.getGenes().set(i, new Random().nextDouble() * 10);
            }
        }
    }
}

这段代码演示了如何用遗传算法实现自动组卷。其中，Solution类代表一个试卷或试卷的一道题目，每个Solution对象都有一个genes属性，代表该试卷或题目的答案。在evolvePopulation方法中，我们首先保留一些精英，然后进行交叉和突变操作来生成新的个体。selectPopulation方法是随机选择一个父代个体对象。crossover方法是通过随机选择交叉点来实现交叉。mutate方法是随机突变一个个体对象的genes属性。