java-如何将测试功能应用于遗传算法
作者：互联网
我已经编写了以下用于种群进化的代码(遗传算法实现)：
个人.java
import java.util.Random;
public class Individual {
public static int SIZE = 500;
private int[] genes = new int[SIZE];
private double fitnessValue = 0.0;
// Getters and Setters
public void setGene(int index,int gene){
this.genes[index] = gene;
}
public int getGene(int index){
return this.genes[index];
}
public void setFitnessValue(double fitness){
this.fitnessValue = fitness;
}
public double getFitnessValue(){
return this.fitnessValue;
}
//Function to generate a new individual with random set of genes
public void generateIndividual(){
Random rand = new Random();
for(int i=0;i<SIZE;i++){
this.setGene(i, rand.nextInt(2));
}
}
//Mutation Function
public void mutate(){
Random rand = new Random();
int index = rand.nextInt(SIZE);
this.setGene(index, 1-this.getGene(index)); // Flipping value of gene
}
//Function to set Fitness value of an individual
public int evaluate(){
int fitness = 0;
for(int i=0; i<SIZE; ++i) {
fitness += this.getGene(i);
}
this.setFitnessValue(fitness);
return fitness;
}
}
人口.java
import java.util.Random;
public class Population {
final static int ELITISM = 5;
final static int POP_SIZE = 200+ELITISM; //Population size + Elitism (1)
final static int MAX_ITER = 10000;
final static double MUTATION_RATE = 0.05;
final static double CROSSOVER_RATE = 0.7;
public static int generation = 2;
private static Random rand = new Random();
private double totalFitness;
private Individual[] pop;
//Constructor
public Population(){
pop = new Individual[POP_SIZE];
//Initialising population
for(int i=0;i<POP_SIZE;i++){
pop[i] = new Individual();
pop[i].generateIndividual();
}
//Evaluating current population
this.evaluate();
}
//Storing new generation in population
public void setPopulation(Individual[] newPop) {
System.arraycopy(newPop, 0, this.pop, 0, POP_SIZE);
}
//Method to find total fitness of population
public double evaluate(){
this.totalFitness = 0.0;
for (int i = 0; i < POP_SIZE; i++) {
this.totalFitness += pop[i].evaluate();
}
return this.totalFitness;
}
//Getters
public Individual getIndividual(int index) {
return pop[index];
}
//Function to find fittest individual for elitism
public Individual getFittest() {
Individual fittest = pop[0];
for (int i = 0; i < POP_SIZE; i++) {
if (fittest.getFitnessValue() <= getIndividual(i).getFitnessValue()) {
fittest = getIndividual(i);
}
}
return fittest;
}
//CROSSOVER Function : Takes 2 individuals and returns 2 new individuals
public static Individual[] crossover(Individual indiv1,Individual indiv2) {
Individual[] newIndiv = new Individual[2];
newIndiv[0] = new Individual();
newIndiv[1] = new Individual();
int randPoint = rand.nextInt(Individual.SIZE);
int i;
for (i=0; i<randPoint; ++i) {
newIndiv[0].setGene(i, indiv1.getGene(i));
newIndiv[1].setGene(i, indiv2.getGene(i));
}
for (; i<Individual.SIZE; ++i) {
newIndiv[0].setGene(i, indiv2.getGene(i));
newIndiv[1].setGene(i, indiv1.getGene(i));
}
return newIndiv;
}
//Roulette Wheel Selection Function
public Individual rouletteWheelSelection() {
double randNum = rand.nextDouble() * this.totalFitness;
int idx;
for (idx=0; idx<POP_SIZE && randNum>0; idx++) {
randNum -= pop[idx].getFitnessValue();
}
return pop[idx-1];
}
//Main method
public static void main(String[] args) {
Population pop = new Population();
Individual[] newPop = new Individual[POP_SIZE];
Individual[] indiv = new Individual[2];
//Current Population Stats
System.out.print("Generation #1");
System.out.println("Total Fitness = "+pop.totalFitness);
System.out.println("Best Fitness = "+pop.getFittest().getFitnessValue());
int count;
for(int iter=0;iter<MAX_ITER;iter++){
count =0;
//Elitism
newPop[count] = pop.getFittest();
count++;
//Creating new population
while(count < POP_SIZE){
//Selecting parents
indiv[0] = pop.rouletteWheelSelection();
indiv[1] = pop.rouletteWheelSelection();
// Crossover
if (rand.nextDouble() < CROSSOVER_RATE ) {
indiv = crossover(indiv[0], indiv[1]);
}
// Mutation
if ( rand.nextDouble() < MUTATION_RATE ) {
indiv[0].mutate();
}
if ( rand.nextDouble() < MUTATION_RATE ) {
indiv[1].mutate();
}
// add to new population
newPop[count] = indiv[0];
newPop[count+1] = indiv[1];
count += 2;
}
// Saving new population in pop
pop.setPopulation(newPop);
//Evaluating new population
pop.evaluate();
System.out.println("Generation #"+ generation++);
System.out.print("Total Fitness = " + pop.totalFitness);
System.out.println(" ; Best Fitness = " +pop.getFittest().getFitnessValue());
}
Individual bestIndiv = pop.getFittest();
}
}
我被要求使用以下功能测试我的算法：
https://en.wikipedia.org/wiki/Test_functions_for_optimization
用于单目标优化的测试功能
谁能解释这是怎么做的？列表中任何一项功能的说明都将有所帮助.
解决方法:
基因应该代表什么
我将假设您的遗传算法的实现是正确的,因为这超出了此问题的范围.
现在,您的适应度函数定义为所有基因的总和：
double fitness = 0;
for(int i=0; i<SIZE; ++i) {
fitness += this.getGene(i);
}
this.setFitnessValue(fitness);
这是一件奇怪的事情：让我们考虑一下单个巫婆的身体健康.我希望您看到没有真正的最佳选择,因为个体将倾向于增加其每个基因,因为这将归档更高的适应性.
第二个问题是基因应该代表某种东西：基因阵列中的两倍实际上意味着什么？我们为什么在乎？一个可能的例子是让它们代表模拟中个人的行为.当然,这是另一个主题,因此我们需要它们表示简单的内容,以便轻松计算其适应性.
让我们让数组的大小为1,假设x = genes [0].个体将只有一个基因：x坐标.现在我们需要定义适应度函数,我们将选择y = 0的Easom.这就是我定义新适应度函数的方式：
double fitness = -cos(x)*cos(0)*exp(-(pow(x-PI,2)+pow(0-PI,2)));
当然,最合适的输入是类别的顶部：
import static java.lang.Math.*;
如果您的程序确实确实针对适应性进行了优化,则应收敛到x = PI.我很快写了我自己的(非常难看)implementation,它确实可以正确收敛.
还有一件事：基因应该是double []而不是int [],因为当x只能是int时,递增优化函数实际上并不起作用.
为什么需要基因阵列？
我认为您的作业希望您使用双精度数组作为基因,因此您最终得到了一个程序,该程序可以使用任何数量的变量来优化任何功能.在编程中,编写可重复用于多种不同事物的代码始终是一个好主意.
随意问任何问题！
我试图尽一切可能解释清楚,但是如果您不明白,请随时提出！
标签：genetic-programming,genetics,java,algorithm,genetic 来源： https://codeday.me/bug/20191111/2023125.html