遗传算法解决迷宫寻路问题（Java实现）

1.什么是遗传算法？
就个人理解，遗传算法是模拟神奇的大自然中生物“优胜劣汰”原则指导下的进化过程，好的基因有更多的机会得到繁衍，这样一来，随着繁衍的进行，生物种群会朝着一个趋势收敛。而生物繁衍过程中的基因杂交和变异会给种群提供更好的基因序列，这样种群的繁衍趋势将会是“长江后浪推前浪，一代更比一代强”，而不会是只受限于祖先的最好基因。而程序可以通过模拟这种过程来获得问题的最优解（但不一定能得到）。要利用该过程来解决问题，受限需要构造初始的基因组，并为对每个基因进行适应性分数（衡量该基因的好坏程度）初始化，接着从初始的基因组中选出两个父基因（根据适应性分数，采用轮盘算法进行选择）进行繁衍，基于一定的杂交率（父基因进行杂交的概率）和变异率（子基因变异的概率），这两个父基因会生成两个子基因，然后将这两个基因放入种群中，到这里繁衍一代完成，重复繁衍的过程直到种群收敛或适应性分数达到最大。
2.利用遗传算法解决迷宫寻路问题。
代码如下：

import java.awt.Color;import java.awt.Graphics;import java.awt.GridLayout;import java.util.ArrayList;import java.util.List;import java.util.Random;import javax.swing.JFrame;import javax.swing.JLabel;import javax.swing.JPanel;@SuppressWarnings("serial")public class MazeProblem extends JFrame{    //当前基因组    private static List<Gene> geneGroup = new ArrayList<>();    private static Random random = new Random();    private static int startX = 2;    private static int startY = 0;    private static int endX = 7;    private static int endY = 14;    //杂交率    private static final double CROSSOVER_RATE = 0.7;    //变异率    private static final double MUTATION_RATE = 0.0001;    //基因组初始个数    private static final int POP_SIZE = 140;    //基因长度    private static final int CHROMO_LENGTH = 70;    //最大适应性分数的基因    private static Gene maxGene = new Gene(CHROMO_LENGTH);    //迷宫地图    private static int[][] map = {{1,1,1,1,1,1,1,1,1,1,1,1,1,1,1},                           {1,0,1,0,0,0,0,0,1,1,1,0,0,0,1},                           {5,0,0,0,0,0,0,0,1,1,1,0,0,0,1},                           {1,0,0,0,1,1,1,0,0,1,0,0,0,0,1},                           {1,0,0,0,1,1,1,0,0,0,0,0,1,0,1},                           {1,1,0,0,1,1,1,0,0,0,0,0,1,0,1},                           {1,0,0,0,0,1,0,0,0,0,1,1,1,0,1},                           {1,0,1,1,0,0,0,1,0,0,0,0,0,0,8},                           {1,0,1,1,0,0,0,1,0,0,0,0,0,0,1},                           {1,1,1,1,1,1,1,1,1,1,1,1,1,1,1}};    private static int MAP_WIDTH = 15;    private static int MAP_HEIGHT = 10;    private List<JLabel> labels = new ArrayList<>();    public MazeProblem(){        // 初始化        setSize(700, 700);        setDefaultCloseOperation(DISPOSE_ON_CLOSE);        setResizable(false);        getContentPane().setLayout(null);        JPanel panel = new JPanel();        panel.setLayout(new GridLayout(MAP_HEIGHT,MAP_WIDTH));        panel.setBounds(10, 10, MAP_WIDTH*40, MAP_HEIGHT*40);        getContentPane().add(panel);        for(int i=0;i<MAP_HEIGHT;i++){            for(int j=0;j<MAP_WIDTH;j++){                JLabel label = new JLabel();                Color color = null;                if(map[i][j] == 1){                    color = Color.black;                }                if(map[i][j] == 0){                    color = Color.GRAY;                }                if(map[i][j] == 5 || map[i][j] ==8){                    color = Color.red;                }                label.setBackground(color);                label.setOpaque(true);                panel.add(label);                labels.add(label);            }        }    }    @Override    public void paint(Graphics g) {        super.paint(g);        //画出路径        int[] gene = maxGene.getGene();        int curX = startX;        int curY = startY;        for(int i=0;i<gene.length;i+=2){            //上            if(gene[i] == 0 && gene[i+1] == 0){                if(curX >=1 && map[curX-1][curY] == 0){                    curX --;                }            }            //下            else if(gene[i] == 0 && gene[i+1] == 1){                if(curX <=MAP_HEIGHT-1 && map[curX+1][curY] == 0){                    curX ++;                }            }            //左            else if(gene[i] == 1 && gene[i+1] == 0){                if(curY >=1 && map[curX][curY-1] == 0){                    curY --;                }            }               //右            else{                if(curY <= MAP_WIDTH-1 && map[curX][curY+1] == 0){                    curY ++;                }            }            labels.get(curX*MAP_WIDTH+curY).setBackground(Color.BLUE);        }    }    public static void main(String[] args) {        //初始化基因组        init();        while(maxGene.getScore() < 1){            //选择进行交配的两个基因            int p1 = getParent(geneGroup);            int p2 = getParent(geneGroup);            //用轮盘转动法选择两个基因进行交配,杂交和变异            mate(p1,p2);        }        new MazeProblem().setVisible(true);    }    /**     * 根据路径获得适应性分数     * @param path     * @return     */    private static double getScore(int[] gene){        double result = 0;        int curX = startX;        int curY = startY;        for(int i=0;i<gene.length;i+=2){            //上            if(gene[i] == 0 && gene[i+1] == 0){                if(curX >=1 && map[curX-1][curY] == 0){                    curX --;                }            }            //下            else if(gene[i] == 0 && gene[i+1] == 1){                if(curX <=MAP_HEIGHT-1 && map[curX+1][curY] == 0){                    curX ++;                }            }            //左            else if(gene[i] == 1 && gene[i+1] == 0){                if(curY >=1 && map[curX][curY-1] == 0){                    curY --;                }            }               //右            else{                if(curY <= MAP_WIDTH-1 && map[curX][curY+1] == 0){                    curY ++;                }            }        }        double x = Math.abs(curX - endX);        double y = Math.abs(curY - endY);        //如果和终点只有一格距离则返回1        if((x == 1&& y==0) || (x==0&&y==1)){            return 1;        }        //计算适应性分数        result = 1/(x+y+1);        return result;    }    /**     * 基因初始化     */    private static void init(){        for(int i=0;i<POP_SIZE;i++){            Gene gene = new Gene(CHROMO_LENGTH);            double score = getScore(gene.getGene());            if(score > maxGene.getScore()){                maxGene = gene;            }            gene.setScore(score);            geneGroup.add(gene);        }    }    /**     * 根据适应性分数随机获得进行交配的父类基因下标     * @param list     * @return     */    private static int getParent(List<Gene> list){        int result  = 0;        double r = random.nextDouble();        double score;        double sum = 0;        double totalScores = getTotalScores(geneGroup);        for(int i=0;i<list.size();i++){            Gene gene = list.get(i);            score = gene.getScore();            sum += score/totalScores;            if(sum >= r){                result = i;                return result;            }        }        return result;    }    /**     * 获得全部基因组的适应性分数总和     * @param list     * @return     */    private static double getTotalScores(List<Gene> list){        double result = 0;        for(int i=0;i<list.size();i++){            result += list.get(i).getScore();        }        return result;    }    /**     * 两个基因进行交配     * @param p1     * @param p2     */    private static void mate(int n1,int n2){        Gene p1 = geneGroup.get(n1);        Gene p2 = geneGroup.get(n2);        Gene c1 = new Gene(CHROMO_LENGTH);        Gene c2 = new Gene(CHROMO_LENGTH);        int[] gene1 = new int[CHROMO_LENGTH];        int[] gene2 = new int[CHROMO_LENGTH];        for(int i=0;i<CHROMO_LENGTH;i++){            gene1[i] = p1.getGene()[i];            gene2[i] = p2.getGene()[i];        }        //先根据杂交率决定是否进行杂交        double r = random.nextDouble();        if(r >= CROSSOVER_RATE){            //决定杂交起点            int n = random.nextInt(CHROMO_LENGTH);            for(int i=n;i<CHROMO_LENGTH;i++){                int tmp = gene1[i];                gene1[i] = gene2[i];                gene2[i] = tmp;            }        }        //根据变异率决定是否        r = random.nextDouble();        if(r >= MUTATION_RATE){            //选择变异位置            int n = random.nextInt(CHROMO_LENGTH);            if(gene1[n] == 0){                gene1[n] = 1;            }            else{                gene1[n] = 0;            }            if(gene2[n] == 0){                gene2[n] = 1;            }            else{                gene2[n] = 0;            }        }        c1.setGene(gene1);        c2.setGene(gene2);        double score1 = getScore(c1.getGene());        double score2 = getScore(c2.getGene());        if(score1 >maxGene.getScore()){            maxGene = c1;        }        if(score2 >maxGene.getScore()){            maxGene = c2;        }        c1.setScore(score1);        c2.setScore(score2);        geneGroup.add(c1);        geneGroup.add(c2);    }}/** * 基因 * @author ZZF * */class Gene{    //染色体长度    private int len;    //基因数组    private int[] gene;    //适应性分数    private double score;    public Gene(int len){        this.len = len;        gene = new int[len];        Random random = new Random();        //随机生成一个基因序列        for(int i=0;i<len;i++){            gene[i] = random.nextInt(2);        }        //适应性分数设置为0        this.score = 0;    }    public int getLen() {        return len;    }    public void setLen(int len) {        this.len = len;    }    public int[] getGene() {        return gene;    }    public void setGene(int[] gene) {        this.gene = gene;    }    public double getScore() {        return score;    }    public void setScore(double score) {        this.score = score;    }    public void print(){        StringBuilder sb = new StringBuilder();        for(int i=0;i<gene.length;i+=2){            if(gene[i] == 0 && gene[i+1] == 0){                sb.append("上");            }            //下            else if(gene[i] == 0 && gene[i+1] == 1){                sb.append("下");            }            //左            else if(gene[i] == 1 && gene[i+1] == 0){                sb.append("左");            }               //右            else{                sb.append("右");            }        }        System.out.println(sb.toString());    }}

3.下面是运行结果截图