遗传算法java版本

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

2、 Best (public int generations; /最佳适应值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 String10;/染色体private int gernation = 0;染色体public static final int G

3、ENE = 5; /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();/*/public SGAFrame() (super();this.ipop = inialPops();getContentPane().setLayout(null);setBounds(100, 100, 461

4、, 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(0,1);label_1.setBounds(92, 10, 84, 15);getContentPane().add(label_1);final JBu

5、tton button = new JButton();button.addActionListener(new ActionListener() (public void actionPerformed(final ActionEvent e) ( SGAFrame s = new SGAFrame();str = str + cess() + n;textArea.setText(str););button.setText(求最大值)；button.setBounds(323, 27, 99, 23);getContentPane().add(button);final JLab

6、el label_2 = new JLabel();label_2.setText(利用标准遗传算法求解函数f(x)=xA2的最大值:);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 = ne

7、w 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 0.5) res += 0; else res += 1;return res;/*初始化一组染色体* return染色体组*/private String in

8、ialPops() String ipop = new String10;for (int i = 0; i 10; i+) ipopi = 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);double x =b;double fitness =x*x;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

9、规则选择将进入下一代的个体;*/private void select() (double evals = new double10; / 所有染色体适应值double p = new double10;/ 各染色体选择概率double q = new double10;/ 累计概率double F = 0;/累计适应值总和for (int i = 0; i best.fitness)( / 记录最优个体best.fitness = evalsi;best.generations = gernation;best.str = ipopi;F = F + evalsi; /所有染色体适应值总和f

10、or (int i = 0; i 10; i+) (pi = evalsi / F;if (i = 0)qi = pi;else (qi = qi - 1 + pi；for (int i = 0; i 10; i+) (double r = Math.random();if (r = q0) (ipopi = ipop0;else (for (int j = 1; j 10; j+) (if (r qj)(ipopi = ipopj;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;fo

11、r (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 = ipopi.substring(0, pos)+ ipop(i + 1) % 10.substring(pos);temp2 = ipop(i + 1) % 10.substring(0, pos)+ ipopi.substring(pos); ipopi = temp1;ipop(i + 1) /

12、 10 = temp2;/*基因变异操作* */private void mutation() (for (int i = 0; i = 10) chromosomeNum = 9;String temp;if (ipopchromosomeNum.charAt(mutationNum - 1) = 0) (if (mutationNum = 1) (temp = 1 + ipopchromosomeNum.substring(mutationNum);else (if (mutationNum != GENE) (temp = ipopchromosomeNum.substring(0, m

13、utationNum -1) + 1+ ipopchromosomeNum.substring(mutationNum);else (temp = ipopchromosomeNum.substring(0, mutationNum -1) + 1;else (if (mutationNum = 1) (temp = 0 + ipopchromosomeNum.substring(mutationNum);else (if (mutationNum != GENE) (temp = ipopchromosomeNum.substring(0, mutationNum -1) + 0+ ipopchromosomeNum.substring(mutationNum);else (temp = ipopchromosomeNum.substring(0, mutation