sgaframe.java

Some good GA alorthim ,just like a good program,you can download it for example

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;
	}
	
}