java遗传算法源程序.txt

java遗传算法程序

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; 

/** 
* 编写者: 赖志环 
* 标准遗传算法求解函数 
* 编写日期: 2007-12-2 
*/ 
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; 
} 

}