遗传算法简单程序应用

2025-02-12 09:17:13

推荐回答（4个）

回答1：

import java.awt.BorderLayout;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import javax.swing.JButton;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JScrollPane;
import javax.swing.JTextArea;
import javax.swing.JTextField;

class Best {
public int generations; //最佳适应值代号
public String str; //最佳染色体
public double fitness; //最佳适应值
}

public class SGAFrame extends JFrame {

private JTextArea textArea;
private String str = "";
private Best best = null; //最佳染色体
private String[] ipop = new String[10]; //染色体
private int gernation = 0; //染色体代号
public static final int GENE = 22; //基因数
/**
* Launch the application
* @param args
*/
public static void main(String args[]) {
try {
SGAFrame frame = new SGAFrame();
frame.setVisible(true);
} catch (Exception e) {
e.printStackTrace();
}
}

/**
* Create the frame
*/
public SGAFrame() {
super();

this.ipop = inialPops();

getContentPane().setLayout(null);
setBounds(100, 100, 461, 277);
setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);

final JLabel label = new JLabel();
label.setText("X的区间:");
label.setBounds(23, 10, 88, 15);
getContentPane().add(label);

final JLabel label_1 = new JLabel();
label_1.setText("[-255,255]");
label_1.setBounds(92, 10, 84, 15);
getContentPane().add(label_1);

final JButton button = new JButton();
button.addActionListener(new ActionListener() {
public void actionPerformed(final ActionEvent e) {
SGAFrame s = new SGAFrame();
str = str + s.process() + "\n";
textArea.setText(str);
}
});
button.setText("求最小值");
button.setBounds(323, 27, 99, 23);
getContentPane().add(button);

final JLabel label_2 = new JLabel();
label_2.setText("利用标准遗传算法求解函数f(x)=(x-5)*(x-5)的最小值:");
label_2.setBounds(23, 31, 318, 15);
getContentPane().add(label_2);

final JPanel panel = new JPanel();
panel.setLayout(new BorderLayout());
panel.setBounds(23, 65, 399, 164);
getContentPane().add(panel);

final JScrollPane scrollPane = new JScrollPane();
panel.add(scrollPane, BorderLayout.CENTER);

textArea = new JTextArea();
scrollPane.setViewportView(textArea);
//
}

/**
* 初始化一条染色体（用二进制字符串表示）
* @return 一条染色体
*/
private String inialPop() {
String res = "";
for (int i = 0; i < GENE; i++) {
if (Math.random() > 0.5) {
res += "0";
} else {
res += "1";
}
}
return res;
}

/**
* 初始化一组染色体
* @return 染色体组
*/
private String[] inialPops() {
String[] ipop = new String[10];
for (int i = 0; i < 10; i++) {
ipop[i] = inialPop();
}
return ipop;
}

/**
* 将染色体转换成x的值
* @param str 染色体
* @return 染色体的适应值
*/
private double calculatefitnessvalue(String str) {
int b = Integer.parseInt(str, 2);
//String str1 = "" + "/n";
double x = -255 + b * (255 - (-255)) / (Math.pow(2, GENE) - 1);
//System.out.println("X = " + x);
double fitness = -(x - 5) * (x - 5);
//System.out.println("f(x)=" + fitness);
//str1 = str1 + "X=" + x + "/n"
//+ "f(x)=" + "fitness" + "/n";
//textArea.setText(str1);

return fitness;
}

/**
* 计算群体上每个个体的适应度值;
* 按由个体适应度值所决定的某个规则选择将进入下一代的个体;
*/
private void select() {
double evals[] = new double[10]; // 所有染色体适应值
double p[] = new double[10]; // 各染色体选择概率
double q[] = new double[10]; // 累计概率
double F = 0; // 累计适应值总和
for (int i = 0; i < 10; i++) {
evals[i] = calculatefitnessvalue(ipop[i]);
if (best == null) {
best = new Best();
best.fitness = evals[i];
best.generations = 0;
best.str = ipop[i];
} else {
if (evals[i] > best.fitness) // 最好的记录下来
{
best.fitness = evals[i];
best.generations = gernation;
best.str = ipop[i];
}
}
F = F + evals[i]; // 所有染色体适应值总和

}
for (int i = 0; i < 10; i++) {
p[i] = evals[i] / F;
if (i == 0)
q[i] = p[i];
else {
q[i] = q[i - 1] + p[i];
}
}
for (int i = 0; i < 10; i++) {

double r = Math.random();
if (r <= q[0]) {
ipop[i] = ipop[0];

} else {
for (int j = 1; j < 10; j++) {
if (r < q[j]) {
ipop[i] = ipop[j];
break;
}
}
}
}
}

/**
* 交叉操作
* 交叉率为25%，平均为25%的染色体进行交叉
*/
private void cross() {
String temp1, temp2;
for (int i = 0; i < 10; i++) {
if (Math.random() < 0.25) {
double r = Math.random();
int pos = (int) (Math.round(r * 1000)) % GENE;
if (pos == 0) {
pos = 1;
}
temp1 = ipop[i].substring(0, pos)
+ ipop[(i + 1) % 10].substring(pos);
temp2 = ipop[(i + 1) % 10].substring(0, pos)
+ ipop[i].substring(pos);
ipop[i] = temp1;
ipop[(i + 1) / 10] = temp2;
}
}
}

/**
* 基因突变操作
* 1%基因变异m*pop_size 共180个基因，为了使每个基因都有相同机会发生变异,
* 需要产生[1--180]上均匀分布的
*/
private void mutation() {
for (int i = 0; i < 4; i++) {
int num = (int) (Math.random() * GENE * 10 + 1);
int chromosomeNum = (int) (num / GENE) + 1; // 染色体号

int mutationNum = num - (chromosomeNum - 1) * GENE; // 基因号
if (mutationNum == 0)
mutationNum = 1;
chromosomeNum = chromosomeNum - 1;
if (chromosomeNum >= 10)
chromosomeNum = 9;
//System.out.println("变异前" + ipop[chromosomeNum]);
String temp;
if (ipop[chromosomeNum].charAt(mutationNum - 1) == '0') {
if (mutationNum == 1) {
temp = "1" + ipop[chromosomeNum].substring

(mutationNum);
} else {
if (mutationNum != GENE) {
temp = ipop[chromosomeNum].substring(0, mutationNum -

1) + "1" + ipop

[chromosomeNum].substring(mutationNum);
} else {
temp = ipop[chromosomeNum].substring(0, mutationNum -
1) + "1";
}
}
} else {
if (mutationNum == 1) {
temp = "0" + ipop[chromosomeNum].substring

(mutationNum);
} else {
if (mutationNum != GENE) {
temp = ipop[chromosomeNum].substring(0, mutationNum -

1) + "0" + ipop

[chromosomeNum].substring(mutationNum);
} else {
temp = ipop[chromosomeNum].substring(0, mutationNum -
1) + "1";
}
}
}
ipop[chromosomeNum] = temp;
//System.out.println("变异后" + ipop[chromosomeNum]);
}
}
/**
* 执行遗传算法
*/
public String process() {
String str = "";
for (int i = 0; i < 10000; i++) {
this.select();
this.cross();
this.mutation();
gernation = i;
}
str = "最小值" + best.fitness + ",第" + best.generations + "个染色体";
return str;
}

}