C++ 手搓遗传算法

// my 1st 遗传算法
#include <math.h>
#include <array>
#include<vector>
#include <random>
#include <iostream>

using std::array, std::cout, std::endl, std::vector;


// 每个个体用 M * N个元素，元素值为0或1的数组表示
constexpr int M = 4;// 个体染色体数
constexpr int N = 4; //每个染色体上的基因点位数
constexpr int Qty = 100;// 种群大小
constexpr double reserve_rate = 0.05;//精英保留率
constexpr double elamilation_rate = 0.4;//末位淘汰率




// x 定义域（搜索范围）
constexpr double LSL = 0.0;
constexpr double USL = 9.0;

double f(double x) 
{
    return x + 10 * sin(5 * x) + 7 * cos(4 * x);
}



array<array<int, N>, M> generate_rand_individuality()
{
    std::random_device rd;
    std::default_random_engine eng(rd());
    std::uniform_int_distribution<int> distr(0, 1);//生成0到1的之间的平均分布的随机整数
    array<array<int, N>, M> I{};
    for (int i = 0; i < M; i++)
    {
        for (int j = 0; j < N; j++)
        {
            I[i][j] = distr(eng);
        }
    }
    return I;
}



void print_individuality(array<array<int, N>, M> I)
{
    for (int i = 0; i < M; i++)
    {
        for (int j = 0; j < N - 1; j++)
        {
            cout << I[i][j] << ',';
        }
        cout << I[i][N - 1] << '\n';
    }
    cout << endl;
}



double decode(const array<array<int, N>, M> & I)
{
    double result = LSL;
    double Max_number = pow(2, M * N) - 1;
    double sum = 0.0;
    for (int i = 0; i < M; i++)
    {
        for (int j = 0; j < N; j++)
        {
            sum += I[i][j] * pow(2, i * N + j);
        }
    }
    result += (USL - LSL) * sum / Max_number;
    return result;
}


double assess(const array<array<int, N>, M> & I)
{
    return f(decode(I));
}



array<array<int, N>, M> reproduce(const array<array<int, N>, M>& I1, const array<array<int, N>, M>& I2)
{
    std::random_device rd;
    std::default_random_engine eng(rd());
    std::uniform_int_distribution<int> distr(0, 1);//生成0到1的之间的平均分布的随机整数
    array<array<int, N>, M> I{};
    for (int i = 0; i < M; i++)
    {
        for (int j = 0; j < N; j++)
        {
            if (I1[i][j] == I2[i][j]) I[i][j] = I1[i][j];
            else I[i][j] = distr(eng);
        }
    }
    return I;
}


struct Grater
{
    bool operator()(const array<array<int, N>, M>& I1, const array<array<int, N>, M>& I2)
{
        return assess(I1) > assess(I2);
    }
};



vector<array<array<int, N>, M>> regroup(const vector<array<array<int, N>, M>>& group)
{ 
    int top_qty    = static_cast<int> (reserve_rate     * Qty);
    int bottom_qty = static_cast<int> (elamilation_rate * Qty);
    vector<array<array<int, N>, M>> new_group{ group };


    int i = top_qty;
    for (int j = 0; j < Qty - bottom_qty; j+=2)
    {
        //除去淘汰的个体外，每两个个体强强结合，生两个仔...
        new_group[i] = reproduce(group[j], group[j+1]);
        new_group[i+1] = reproduce(group[j], group[j + 1]);
        i += 2;
    }
    //淘汰的个体用随机产生的个体替代掉
    for (int i = Qty - bottom_qty; i < Qty; i++)
    {
        new_group[i] = generate_rand_individuality();
    }
    return new_group;
}




int main()
{
    //std::cout << f(7.0) << '\n';
    //std::random_device rd;
    //std::default_random_engine eng(rd());
    //std::uniform_int_distribution<int> distr(0, 1);
    //array<array<int, N>, M> a{ generate_rand_individuality() };
    //array<array<int, N>, M> b{ generate_rand_individuality() };
    //array<array<int, N>, M> c{};
    //c = reproduce(a, b);
    ////array<array<int, N>, M> b{ 1,0,0,0 };
    //print_individuality(a);
    //cout << assess(a) << endl;


    //print_individuality(b);
    //cout << assess(b) << endl;


    //print_individuality(c);
    //cout << assess(c) << endl;


    vector<array<array<int, N>, M>> group{};
    for (int i = 0; i < Qty; i++)
    {
        group.push_back(generate_rand_individuality());
    }


    std::sort(group.begin(), group.end(), Grater());


    //  打印初代评分
    cout << "初代评分：" << endl;
    for (auto &I : group)
    {
        cout << assess(I) << ", ";
    }


    cout << endl;
    cout << endl;


    //迭代
    cout << "每代的第一名的得分：" << endl;
    for(int i =0; i<100; i++)
        {
            group = regroup(group);
            std::sort(group.begin(), group.end(), Grater());
            cout << assess(group.at(0))<<"-> ";
        }
    cout << endl;
    cout << endl;


    //  打印迭代完成后的评分
    cout << "迭代完成后的评分：" << endl;
    for (auto& I : group)
    {
        cout << assess(I) << ", ";
    }
    cout << endl;
    cout <<"迭代完成后的最优基因是" << endl;
    print_individuality(group.at(0));//打印最优基因
    double x = decode(group.at(0));
    cout << "求得的x的值是" << x << endl;
    cout << "此时 x 对应的函数值是" << f(x) << endl;
}