carfield.com.hk

Genetic.class

2000-06-16T16:00:00Z

Genetic.java

2000-06-16T16:00:00Z

// Genetic Algorithm Java classes
//
// Copyright 1996, Mark Watson. All rights reserved.

import java.util.*;

abstract public class Genetic {

 protected int numGenesPerChromosome; // number of genes per chromosome
 protected int numChromosomes; // number of chromosomes
 private BitSet chromosomes[];
 protected float fitness[];
 private float crossoverFraction;
 private float mutationFraction;
 private int [] rouletteWheel;
 private int rouletteWheelSize;

 public Genetic(int num_genes_per_chromosome, int num_chromosomes) {
 this(num_genes_per_chromosome, num_chromosomes, 0.8f, 0.01f);
 }
 public Genetic(int num_genes_per_chromosome, int num_chromosomes,
		 float crossover_fraction, float mutation_fraction) {
 numGenesPerChromosome = num_genes_per_chromosome;
 numChromosomes = num_chromosomes;
 crossoverFraction = crossover_fraction;
 mutationFraction = mutation_fraction;
 chromosomes = new BitSet[num_chromosomes];
 for (int i=0; i<num_chromosomes; i++) {
 chromosomes[i] = new BitSet(num_genes_per_chromosome);
 for (int j=0; j<num_genes_per_chromosome; j++) {
 if (Math.random() < 0.5) chromosomes[i].set(j);
 }
 }
 fitness = new float[num_chromosomes];
 for (int f=0; f<num_chromosomes; f++) {
 fitness[f] = -999;
 }
 sort();
 // define the roulette wheel:
 rouletteWheelSize = 0;
 for (int i=0; i<numGenesPerChromosome; i++) {
	 rouletteWheelSize += i + 1;
 }
 System.out.println("count of slots in roulette wheel=" + rouletteWheelSize);
 rouletteWheel = new int[rouletteWheelSize];
 int num_trials = numGenesPerChromosome;
 int index = 0;
 for (int i=0; i<numGenesPerChromosome; i++) {
	 for (int j=0; j<num_trials; j++) {
	 rouletteWheel[index++] = i;
	 }
	 num_trials--;
 }
 }
 public boolean getGene(int chromosome, int gene) {
 return chromosomes[chromosome].get(gene);
 }
 public void setGene(int chromosome, int gene, int value) {
 if (value == 0) chromosomes[chromosome].clear(gene);
 else chromosomes[chromosome].set(gene);
 }
 public void setGene(int chromosome, int gene, boolean value) {
 if (value) chromosomes[chromosome].set(gene);
 else chromosomes[chromosome].clear(gene);
 }
 public void sort() {
 BitSet btemp;
 for (int c=0; c<numChromosomes; c++) {
 for (int d=(numChromosomes - 2); d>=c; d--) {
 if (fitness[d] < fitness[d+1]) {
 btemp = chromosomes[d];
 float x = fitness[d];
 chromosomes[d] = chromosomes[d+1];
 fitness[d] = fitness[d+1];
 chromosomes[d+1] = btemp;
 fitness[d+1] = x;
 }
 }
 }
 }
 public void evolve() {
	calcFitness();
	sort();
	doCrossovers();
	doMutations();
	doRemoveDuplicates();
 }
 public void doCrossovers() {
 int num = (int)(numChromosomes * crossoverFraction);
 for (int i=num-1; i>=0; i--) {
	 int c1 = (int)(rouletteWheelSize * Math.random() * 0.9999f);
	 int c2 = (int)(rouletteWheelSize * Math.random() * 0.9999f);
	 c1 = rouletteWheel[c1];
	 c2 = rouletteWheel[c2];
	 if (c1 != c2) {
	 int locus = 1 + (int)((numGenesPerChromosome - 2) * Math.random());
	 for (int g=0; g<numGenesPerChromosome; g++) {
		 if (g < locus) {
		 setGene(i, g, getGene(c1, g));
		 } else {
		 setGene(i, g, getGene(c2, g));
		 }
	 }
	 }
 }
 }
 // 'to' and 'from' are 'sorted' indices:
 private void copyGene(int to, int from) {
 for (int i=0; i<numGenesPerChromosome; i++)
 if (getGene(from, i)) setGene(to, i, 1);
 else setGene(to, i, 0);
 }
 public void doMutations() {
 int num = (int)(numChromosomes * mutationFraction);
 for (int i=0; i<num; i++) {
	 int c = (int)(numChromosomes * Math.random() * 0.99);
	 int g = (int)(numGenesPerChromosome * Math.random() * 0.99);
	 setGene(c, g, !getGene(c, g));
 }
 }
 public void doRemoveDuplicates() {
	for (int i=numChromosomes - 1; i>3; i--) {
	 for (int j=0; j<i; j++) {
		if (chromosomes[i].equals(chromosomes[j])) {
		 int g = (int)(numGenesPerChromosome * Math.random() * 0.99);
		 setGene(i, g, !getGene(i, g));
		 break;
		}
	 }
	}
 }
 // Override the following function in sub-classes:
 abstract public void calcFitness() ;
}

MyGenetic.class

2000-06-16T16:00:00Z

RegressionGenetic.class

2000-06-16T16:00:00Z

RegressionTest.class

2000-06-16T16:00:00Z

RegressionTest.java

2000-06-16T16:00:00Z

// File: RegressionTest.java
// This file contains a text-mode test program
// for class Genetic for doing a polynomial regression test.

public class RegressionTest {

 static RegressionGenetic g;

 static public void main(String args[]) {
 // we will use chromosomes with 10 1 bit genes per
 // floating pouint number (20 bits for 5 numbers)
 // and a population of 100 chromosomes:
 g = new RegressionGenetic(30, 5000, 0.98f, 0.15f);
 System.out.println("calcNormalization(10)=" + g.calcNormalization(10));
 System.out.println("calcNormalization(20)=" + g.calcNormalization(20));
 System.out.println("calcNormalization(5)=" + g.calcNormalization(5));
 for (int i=0; i<501; i++) {
 g.evolve();
 if ((i%50)==0 || i < 8) {
 System.out.println("Generation " + i);
 g.print();
 }
 }
 System.out.println("gmin="+RegressionGenetic.gmin+", gmax="+RegressionGenetic.gmax);
 }
}

class RegressionGenetic extends Genetic {
 RegressionGenetic(int num_g, int num_c, float crossover_fraction,
 float mutation_fraction) {
 super(num_g, num_c, crossover_fraction, mutation_fraction);
 if ((num_g / 5)*5 != num_g) {
 System.out.println("Error, number of genes per chromosome must be evenly divisible by 5");
 System.exit(1);
 }
 bitsPerNumber = num_g / 5;
 normalization = calcNormalization(bitsPerNumber);
 }
 public float calcNormalization(int num_bits) {
 float base = 1.0f;
 float ret = 0.0f;
 for (int i=0; i<num_bits - 3; i++) {
 ret += base;
 base *= 2;
 }
 return base;
 }

 private int bitsPerNumber;
 private float normalization;
 private float calcFitnessForCoefficients(float [] coef) {
 float ret = 0.0f;
 // evaluate function error over a range of values of x:
 for (float x=0.0f; x<=10.0f; x+= 0.05) {
 // true value of regression function:
 float Ftrue = x*x*x*x + x*x*x + x*x + x;
 // value of generated function at x:
 float F = coef[0] * x*x*x*x +
 coef[1] * x*x*x +
 coef[2] * x*x +
 coef[3] * x +
 coef[4];
 //ret += (float)Math.sqrt((F - Ftrue) * (F - Ftrue));
 ret += (F - Ftrue) * (F - Ftrue);
 }
 return 25000.0f - ret;
 }
 public static float gmin = 10000000000.0f;
 public static float gmax = -gmin;
 float geneToFloat(int chromosomeIndex, int numberIndex) {
 int base = 1;
 float x = 0;
 for (int j=0; j<bitsPerNumber; j++) {
 if (getGene(chromosomeIndex, j + numberIndex * bitsPerNumber)) {
 x += base;
 }
 base *= 2;
 }
 x /= normalization;
 x -= 5.0f;
 if (gmin > x) gmin = x;
 if (gmax < x) gmax = x;
 return x;
 }
 // more efficient to allocate this only once:
 private float [] coefficients = new float[5];
 public void calcFitness() {
 for (int i=0; i<numChromosomes; i++) {
 for (int j=0; j<5; j++) {
 coefficients[j] = geneToFloat(i, j);
 }
 fitness[i] = calcFitnessForCoefficients(coefficients);
 }
 }
 public void print() {
 sort();
 int maxindex = 0;
 float maval = 0.0f;
 float sum = 0.0f;
 for (int i=0; i<numChromosomes; i++) {
 for (int j=0; j<5; j++) {
 coefficients[j] = geneToFloat(i, j);
 }
 sum += fitness[i];
 if (i < 4 || (i % 100) == 0) {
 System.out.print("Fitness for chromosome " + i + " is ");
 System.out.print(fitness[i] + " coefficients:");
 for (int j=0; j<5; j++) System.out.print(pp(coefficients[j]));
 System.out.println();
 }
 }
 sum /= (float)numChromosomes;
 System.out.println("Average fitness=" + sum);
 }
 private String pp(float x) {
 x = (int)(1000 * x);
 x /= 1000.0f;
 return "" + x + " ";
 }
}

TestGenetic.class

2000-06-16T16:00:00Z

TestGenetic.java

2000-06-16T16:00:00Z

// File: textGenetic.java
// This file contains a text-mode test program
// for class Genetic.

public class TestGenetic {

 static MyGenetic g;

 static public void main(String args[]) {
 // we will use chromosomes with 10 1 bit genes per
 // chromosomes, and a population of 12 chromosomes:
 g = new MyGenetic(10, 30, 0.85f, 0.2);
 for (int i=0; i<21; i++) {
 g.evolve();
 //g.calcFitness();
 //if ((i%5)==0) {
 System.out.println("Generation " + i);
 g.print();
 //}
 }
 }
}

class MyGenetic extends Genetic {
 MyGenetic(int num_g, int num_c, float crossover_fraction,
 float mutation_fraction) {
 super(num_g, num_c, crossover_fraction, mutation_fraction);
 }
 private float fitness(float x) {
 return (float)(Math.sin(x) * Math.sin(0.4f * x) * Math.sin(3.0f * x));
 }
 float geneToFloat(int geneIndex) {
 int base = 1;
 float x = 0;
 for (int j=0; j<numGenesPerChromosome; j++) {
 if (getGene(geneIndex, j)) {
 x += base;
 }
 base *= 2;
 }
 x /= 128.0f;
 return x;
 }
 public void calcFitness() {
 for (int i=0; i<numChromosomes; i++) {
 float x = geneToFloat(i);
 fitness[i] = fitness(x);
 }
 }
 public void print() {
 sort();
 int maxindex = 0;
 float maval = 0.0f;
 float sum = 0.0f;
 for (int i=0; i<numChromosomes; i++) {
 float x = geneToFloat(i);
 sum += fitness[i];
 if (i < 6) {
 System.out.print("Fitness for chromosome ");
 System.out.print(i);
 System.out.print(" is ");
 System.out.println(fitness[i] + ", occurs at x=" + x);
 }
 }
 sum /= (float)numChromosomes;
 System.out.println("Average fitness=" + sum);
 }
}

Graph.class

2000-06-15T16:00:00Z

Graph.java

2000-06-15T16:00:00Z

import javax.swing.*;
import java.awt.*;
/**
 * Title: Graph
 * Description: Simple program to produce a graph for the book
 * Copyright: Copyright (c) Mark Watson
 * Company: 
 * @author Mark Watson
 * @version 1.0
 */

public class Graph extends JFrame {
 GraphPanel jPanel1;
 float [] data;
 public Graph() {
 try {
 int size = 500;
 data = new float[size];
 float xmin = 0;
 float xmax = 10;
 for (int i=0; i<size; i++) {
 float x = i;
 x = xmin + x * (xmax - xmin) / (float)size;
 if (i==190) System.out.println("x="+x);
 float x1 = (float)Math.sin(x);
 float x2 = (float)Math.sin(x * 0.4f);
 float x3 = (float)Math.sin(x * 3.0f);
 data[i] = x1 * x2 * x3;
 }
 jbInit();
 }
 catch(Exception e) {
 e.printStackTrace();
 }
 }

public static void main(String[] args) {
    Graph untitled11 = new Graph();
  }

private void jbInit() throws Exception {
    jPanel1 = new GraphPanel(data);
    jPanel1.setBackground(Color.white);
    this.setDefaultCloseOperation(3);
    this.getContentPane().add(jPanel1, BorderLayout.CENTER);
    setSize(550, 500);
    jPanel1.setVisible(true);
    setVisible(true);
  }
}

GraphPanel.class

2000-06-15T16:00:00Z

GraphPanel.java

2000-06-15T16:00:00Z

/**
 * Title: 
 * Description: 
 * Copyright: Copyright (c) 
 * Company: 
 * @author
 * @version 1.0
 */
import javax.swing.*;
import java.awt.*;

public class GraphPanel extends java.awt.Canvas { // JPanel {

public GraphPanel(float [] data) {
 super();
 this.data = data;
 }
 Color black = new Color(0, 0, 0);
 float [] data;
 public void paint(Graphics g) {
 if (data == null) return;
 int width = this.getWidth();
 int height = this.getHeight();
 System.out.println("height="+height);
 float min = 99999999.9f;
 float max = -min;
 int maxindex = 0;
 float maxval = 0.0f;
 for (int i=0; i<data.length; i++) {
 if (min > data[i]) min = data[i];
 if (max < data[i]) {
 max = data[i];
 maxindex = i;
 maxval = max;
 }
 }
 System.out.println("min=" + min +", max=" + max +
 ", max index="+maxindex + ", max val="+maxval);
 g.setColor(black);
 for (int i=0; i<data.length - 1; i++) {
 float y1 = height - 5 - 0.95f *height * ((data[i] - min) / (max - min));
 float y2 = height - 5 - 0.95f *height * ((data[i+1] - min) / (max - min));
 //System.out.println("data["+i+"]="+data[i]+", y1="+y1+", y2="+y2);
 g.drawLine(i+20, (int)y1, i+21, (int)y2);
 }
 float yzero = height - 5 - 0.95f *height * ((0.0f - min) / (max - min));
 g.drawLine(20, (int)yzero, data.length + 19, (int)yzero);
 }
}

UnnamedPackage.dependency

2000-06-15T16:00:00Z

dependency cache.dfPackage

2000-06-15T16:00:00Z

graph.jpr

2000-06-15T16:00:00Z

misc.dfPackage

2000-06-15T16:00:00Z

ga.jpr

2000-04-02T16:00:00Z