User class ControlArc {std::string star; std::string startTime; std::string endTime; std::string circleId; };由上面类组成的std::vector<std::vector<ControlArc>> controlArcList，在task.h文件的plan类中已经通过读外部json实例化，遗传算法每个染色体由4个基因，每个基因都是controlArcList 随机产生一定个数的vector<ControlArc>组成的数组，适应度函数是每条染色体上（endTime-startTime）值最小，请给出完整的c++遗传算法代码，其中要写.h文件和.cpp文件

下面是遗传算法的C++实现，包括.h文件和.cpp文件，代码中使用了遗传算法的常用基本操作，包括初始化种群、选择、交叉、变异等。其中，适应度函数为计算每个染色体上（endTime-startTime）值的最小值。

task.h：

#ifndef TASK_H
#define TASK_H

#include <vector>
#include <string>

class ControlArc {
public:
    std::string star;
    std::string startTime;
    std::string endTime;
    std::string circleId;
};

class Plan {
public:
    std::vector<std::vector<ControlArc>> controlArcList;

    Plan() {}
    Plan(std::vector<std::vector<ControlArc>> controlArcList) {
        this->controlArcList = controlArcList;
    }
};

#endif // TASK_H

ga.h：

#ifndef GA_H
#define GA_H

#include <vector>
#include "task.h"

class GA {
public:
    GA(int popSize, int maxGen, double pc, double pm, int geneSize, int arcNum, std::vector<std::vector<ControlArc>> controlArcList);
    void run();
    double fitness(Plan& p);
private:
    int popSize; // 种群规模
    int maxGen; // 最大迭代次数
    double pc; // 交叉概率
    double pm; // 变异概率
    int geneSize; // 染色体长度
    int arcNum; // vector<ControlArc> 数组中元素的个数
    std::vector<std::vector<ControlArc>> controlArcList; // 控制弧列表
    std::vector<Plan> pop; // 种群
    Plan bestIndiv; // 最优个体
    double bestFitness; // 最优适应度
    Plan decode(std::vector<std::vector<ControlArc>> gene);
    std::vector<std::vector<ControlArc>> encode(Plan indiv);
    void initPop();
    void select(std::vector<Plan>& newPop);
    void crossover(Plan& p1, Plan& p2);
    void mutate(Plan& indiv);
};

#endif // GA_H

ga.cpp：

#include <algorithm>
#include <cstdlib>
#include <ctime>
#include <iostream>
#include "ga.h"

GA::GA(int popSize, int maxGen, double pc, double pm, int geneSize, int arcNum, std::vector<std::vector<ControlArc>> controlArcList) {
    this->popSize = popSize;
    this->maxGen = maxGen;
    this->pc = pc;
    this->pm = pm;
    this->geneSize = geneSize;
    this->arcNum = arcNum;
    this->controlArcList = controlArcList;
    this->bestFitness = 9999999.0;
}

void GA::run() {
    srand((unsigned int)time(NULL)); // 随机数生成器初始化
    initPop(); // 初始化种群
    for (int i = 0; i < maxGen; i++) { // 迭代maxGen次
        std::vector<Plan> newPop; // 新种群
        select(newPop); // 选择
        for (int j = 0; j < popSize / 2; j++) { // 交叉
            Plan& p1 = newPop[j * 2];
            Plan& p2 = newPop[j * 2 + 1];
            crossover(p1, p2);
        }
        for (int j = 0; j < popSize; j++) { // 变异
            mutate(newPop[j]);
        }
        pop = newPop;
        for (int j = 0; j < popSize; j++) { // 更新最优个体和最优适应度
            double fit = fitness(pop[j]);
            if (fit < bestFitness) {
                bestFitness = fit;
                bestIndiv = pop[j];
            }
        }
        std::cout << "第" << i + 1 << "代，最优适应度：" << bestFitness << std::endl;
    }
}

double GA::fitness(Plan& p) {
    std::vector<std::vector<ControlArc>> gene = encode(p);
    double minEndTime = 9999999.0;
    for (int i = 0; i < gene.size(); i++) {
        std::vector<ControlArc>& arcs = gene[i];
        double endTime = 0.0;
        for (int j = 0; j < arcs.size(); j++) {
            double t = atof(arcs[j].endTime.c_str()) - atof(arcs[j].startTime.c_str());
            if (t > 0) { // 时间差小于0的不计算
                endTime += t;
            }
        }
        if (endTime < minEndTime) {
            minEndTime = endTime;
        }
    }
    return minEndTime;
}

Plan GA::decode(std::vector<std::vector<ControlArc>> gene) {
    return Plan(gene);
}

std::vector<std::vector<ControlArc>> GA::encode(Plan indiv) {
    return indiv.controlArcList;
}

void GA::initPop() {
    for (int i = 0; i < popSize; i++) {
        std::vector<std::vector<ControlArc>> gene(geneSize, std::vector<ControlArc>(arcNum));
        for (int j = 0; j < geneSize; j++) {
            for (int k = 0; k < arcNum; k++) {
                gene[j][k] = controlArcList[rand() % controlArcList.size()][rand() % controlArcList[0].size()];
            }
        }
        pop.push_back(decode(gene));
    }
    bestIndiv = pop[0];
    bestFitness = fitness(bestIndiv);
}

void GA::select(std::vector<Plan>& newPop) {
    double sumFitness = 0.0;
    std::vector<double> fitnessArr(popSize);
    for (int i = 0; i < popSize; i++) {
        double fit = fitness(pop[i]);
        sumFitness += fit;
        fitnessArr[i] = sumFitness;
    }
    for (int i = 0; i < popSize; i++) {
        double fit = (double)rand() / RAND_MAX * sumFitness;
        int j;
        for (j = 0; j < popSize - 1; j++) {
            if (fit <= fitnessArr[j]) {
                break;
            }
        }
        newPop.push_back(pop[j]);
    }
}

void GA::crossover(Plan& p1, Plan& p2) {
    if ((double)rand() / RAND_MAX < pc) { // 满足概率要求
        int point1 = rand() % geneSize;
        int point2 = rand() % geneSize;
        if (point1 > point2) { // 确保point1小于point2
            int temp = point1;
            point1 = point2;
            point2 = temp;
        }
        std::vector<std::vector<ControlArc>> gene1 = encode(p1);
        std::vector<std::vector<ControlArc>> gene2 = encode(p2);
        for (int i = point1; i <= point2; i++) { // 交叉基因
            std::vector<ControlArc> temp = gene1[i];
            gene1[i] = gene2[i];
            gene2[i] = temp;
        }
        p1 = decode(gene1);
        p2 = decode(gene2);
    }
}

void GA::mutate(Plan& indiv) {
    if ((double)rand() / RAND_MAX < pm) { // 满足概率要求
        std::vector<std::vector<ControlArc>> gene = encode(indiv);
        int point = rand() % geneSize;
        for (int i = 0; i < arcNum; i++) { // 变异基因
            gene[point][i] = controlArcList[rand() % controlArcList.size()][rand() % controlArcList[0].size()];
        }
        indiv = decode(gene);
    }
}

使用方法：

#include <vector>
#include <fstream>
#include "ga.h"
#include "task.h"
using namespace std;

int main()
{
    int popSize = 50; // 种群规模
    int maxGen = 100; // 最大迭代次数
    double pc = 0.8; // 交叉概率
    double pm = 0.2; // 变异概率
    int geneSize = 4; // 染色体长度
    int arcNum = 10; // vector<ControlArc> 数组中元素的个数

    // 读取控制弧列表
    ifstream fin("controlArcList.json");
    nlohmann::json j;
    fin >> j;
    vector<vector<ControlArc>> controlArcList = j.get<vector<vector<ControlArc>>>();

    GA ga(popSize, maxGen, pc, pm, geneSize, arcNum, controlArcList);
    ga.run();

    return 0;
}