使用遗传算法通过java编写护士排班的代码并写出individual.calcFitness()的代码

以下是使用遗传算法编写护士排班的Java代码：

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

public class NurseScheduling {
    private int numNurses; // 护士数量
    private int numShifts; // 班次数量
    private int numDays; // 总天数
    private int[][] cover; // 覆盖需求
    private int popSize; // 种群大小
    private double mutationRate; // 变异率
    private int elitismCount; // 精英数量
    private int tournamentSize; // 锦标赛选择数量

    public NurseScheduling(int numNurses, int numShifts, int numDays, int[][] cover, int popSize, double mutationRate, int elitismCount, int tournamentSize) {
        this.numNurses = numNurses;
        this.numShifts = numShifts;
        this.numDays = numDays;
        this.cover = cover;
        this.popSize = popSize;
        this.mutationRate = mutationRate;
        this.elitismCount = elitismCount;
        this.tournamentSize = tournamentSize;
    }

    // 初始化种群
    public Population initPopulation() {
        Population population = new Population(this.popSize, this.numNurses);
        return population;
    }

    // 评估个体适应度
    public double calcFitness(Individual individual) {
        // 初始化每个班次的被覆盖情况
        int[][] shiftCover = new int[this.numShifts][this.numDays];
        for (int i = 0; i < this.numShifts; i++) {
            for (int j = 0; j < this.numDays; j++) {
                shiftCover[i][j] = 0;
            }
        }

        // 统计个体班次被分配情况
        int[][] nurseShifts = individual.getNurseShifts();
        for (int i = 0; i < this.numDays; i++) {
            for (int j = 0; j < this.numNurses; j++) {
                int shiftIndex = nurseShifts[i][j];
                shiftCover[shiftIndex][i] += this.cover[j][i];
            }
        }

        // 计算每个班次的被覆盖程度
        double fitness = 0;
        for (int i = 0; i < this.numShifts; i++) {
            for (int j = 0; j < this.numDays; j++) {
                int demand = this.cover[this.numNurses][j];
                int cover = shiftCover[i][j];
                if (cover >= demand) {
                    fitness += 1;
                }
            }
        }

        // 计算平均适应度
        fitness /= (this.numShifts * this.numDays);
        return fitness;
    }

    // 选择操作
    public Population select(Population population) {
        Population newPopulation = new Population(this.popSize, this.numNurses);

        // 添加精英个体
        for (int i = 0; i < this.elitismCount; i++) {
            Individual elitism = population.getFittest(i);
            newPopulation.setIndividual(i, elitism);
        }

        // 锦标赛选择
        for (int i = this.elitismCount; i < this.popSize; i++) {
            Individual indiv1 = tournamentSelection(population);
            Individual indiv2 = tournamentSelection(population);
            Individual newIndiv = crossover(indiv1, indiv2);
            newPopulation.setIndividual(i, newIndiv);
        }

        return newPopulation;
    }

    // 锦标赛选择
    public Individual tournamentSelection(Population population) {
        Population tournament = new Population(this.tournamentSize, this.numNurses);
        for (int i = 0; i < this.tournamentSize; i++) {
            int randomIndex = (int) (Math.random() * population.size());
            tournament.setIndividual(i, population.getIndividual(randomIndex));
        }
        Individual fittest = tournament.getFittest(0);
        return fittest;
    }

    // 交叉操作
    public Individual crossover(Individual indiv1, Individual indiv2) {
        Individual newSol = new Individual(this.numNurses);
        for (int i = 0; i < this.numDays; i++) {
            if (Math.random() <= 0.5) {
                newSol.setNurseShifts(i, indiv1.getNurseShifts(i));
            } else {
                newSol.setNurseShifts(i, indiv2.getNurseShifts(i));
            }
        }
        return newSol;
    }

    // 变异操作
    public Individual mutate(Individual indiv) {
        Random random = new Random();
        Individual newSol = new Individual(this.numNurses);
        for (int i = 0; i < this.numDays; i++) {
            int[] nurseShifts = indiv.getNurseShifts(i);
            for (int j = 0; j < this.numNurses; j++) {
                if (Math.random() <= this.mutationRate) {
                    nurseShifts[j] = random.nextInt(this.numShifts);
                }
            }
            newSol.setNurseShifts(i, nurseShifts);
        }
        return newSol;
    }

    // 进化操作
    public Population evolve(Population population) {
        Population newPopulation = select(population);
        for (int i = this.elitismCount; i < this.popSize; i++) {
            newPopulation.setIndividual(i, mutate(newPopulation.getIndividual(i)));
        }
        return newPopulation;
    }

    // 执行遗传算法
    public Individual geneticAlgorithm(int numGenerations) {
        Population population = initPopulation();
        for (int i = 0; i < numGenerations; i++) {
            population = evolve(population);
        }
        return population.getFittest(0);
    }
}

在上面的代码中，我们需要实现 `calcFitness()` 方法来评估个体的适应度。该方法会计算每个班次的被覆盖情况，并计算出每个班次的被覆盖程度。然后，我们将每个班次的被覆盖程度求和，再除以总班次数和总天数，得到平均适应度。

以下是 `calcFitness()` 方法的代码：

public double calcFitness(Individual individual) {
    // 初始化每个班次的被覆盖情况
    int[][] shiftCover = new int[this.numShifts][this.numDays];
    for (int i = 0; i < this.numShifts; i++) {
        for (int j = 0; j < this.numDays; j++) {
            shiftCover[i][j] = 0;
        }
    }

    // 统计个体班次被分配情况
    int[][] nurseShifts = individual.getNurseShifts();
    for (int i = 0; i < this.numDays; i++) {
        for (int j = 0; j < this.numNurses; j++) {
            int shiftIndex = nurseShifts[i][j];
            shiftCover[shiftIndex][i] += this.cover[j][i];
        }
    }

    // 计算每个班次的被覆盖程度
    double fitness = 0;
    for (int i = 0; i < this.numShifts; i++) {
        for (int j = 0; j < this.numDays; j++) {
            int demand = this.cover[this.numNurses][j];
            int cover = shiftCover[i][j];
            if (cover >= demand) {
                fitness += 1;
            }
        }
    }

    // 计算平均适应度
    fitness /= (this.numShifts * this.numDays);
    return fitness;
}

在这个方法中，我们首先初始化每个班次的被覆盖情况。然后，我们统计个体班次被分配情况，计算出每个班次在每一天的被覆盖程度。接下来，我们计算每个班次的被覆盖程度是否满足需求，并将满足需求的班次数累加。最后，我们将累加结果除以总班次数和总天数，得到平均适应度。