用Java写个遗传算法基础模型
时间: 2023-07-24 20:17:00 浏览: 55
好的,以下是一个简单的遗传算法基础模型的Java代码。
```java
import java.util.Random;
public class GeneticAlgorithm {
private int populationSize;
private double mutationRate;
private double crossoverRate;
private int elitismCount;
public GeneticAlgorithm(int populationSize, double mutationRate, double crossoverRate, int elitismCount) {
this.populationSize = populationSize;
this.mutationRate = mutationRate;
this.crossoverRate = crossoverRate;
this.elitismCount = elitismCount;
}
public Population evolvePopulation(Population population) {
Population newPopulation = new Population(populationSize);
for (int i = 0; i < elitismCount; i++) {
newPopulation.setIndividual(i, population.getFittest(i));
}
for (int i = elitismCount; i < populationSize; i++) {
Individual parent1 = rouletteWheelSelection(population);
Individual parent2 = rouletteWheelSelection(population);
Individual child = crossover(parent1, parent2);
mutate(child);
newPopulation.setIndividual(i, child);
}
return newPopulation;
}
private Individual rouletteWheelSelection(Population population) {
double fitnessSum = 0;
for (Individual individual : population.getIndividuals()) {
fitnessSum += individual.getFitness();
}
Random random = new Random();
double randomFitness = random.nextDouble() * fitnessSum;
double currentSum = 0;
for (Individual individual : population.getIndividuals()) {
currentSum += individual.getFitness();
if (currentSum > randomFitness) {
return individual;
}
}
return null;
}
private Individual crossover(Individual parent1, Individual parent2) {
Individual child = new Individual(parent1.getChromosomeLength());
Random random = new Random();
for (int i = 0; i < parent1.getChromosomeLength(); i++) {
if (random.nextDouble() <= crossoverRate) {
child.setGene(i, parent1.getGene(i));
} else {
child.setGene(i, parent2.getGene(i));
}
}
return child;
}
private void mutate(Individual individual) {
Random random = new Random();
for (int i = 0; i < individual.getChromosomeLength(); i++) {
if (random.nextDouble() <= mutationRate) {
int gene = individual.getGene(i);
if (gene == 0) {
individual.setGene(i, 1);
} else {
individual.setGene(i, 0);
}
}
}
}
}
```
这段代码包含一个GeneticAlgorithm类,它接受一些参数来配置遗传算法的操作。evolvePopulation方法接受一个Population对象并返回一个新的Population对象。这个方法遵循标准的遗传算法步骤:选择精英个体、选择父母、交叉、突变。其中,rouletteWheelSelection方法使用轮盘赌选择法来选择父母,crossover方法实现单点交叉,mutate方法实现单点突变。
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