} else {

//Do selection

//Do crossover
demo.crossover();

//Do mutation under a random probability
if (rn.nextInt()%7 < 5) {
}

//Add fittest offspring to population
demo.addFittestOffspring();

}
//Calculate new fitness value
demo.population.calculateFitness();

System.out.println("Generation: " + demo.generationCount + " Fittest: " + demo.population.fittest);
}

System.out.println("\nSolution found in generation " + demo.generationCount);
System.out.println("Fitness: "+demo.population.getFittest().fitness);
System.out.print("Genes: ");
for (int i = 0; i < 5; i++) {
System.out.print(demo.population.getFittest().genes[i]);
}

System.out.println("");
}

种群的范围恒定。新一代形成时，适应度最差的个别凋亡，为后代留出空间。这些阶段的序列被赓续反复，以产生优于先前的新一代。

//Selection
void selection() {

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fittest = population.getFittest();

//Select the second most fittest individual
secondFittest = population.getSecondFittest();
}

//Crossover
void crossover() {
Random rn = new Random();

//Select a random crossover point
int crossOverPoint = rn.nextInt(population.individuals[0].geneLength);
//Swap values among parents
for (int i = 0; i < crossOverPoint; i++) {
int temp = fittest.genes[i];
fittest.genes[i] = secondFittest.genes[i];
secondFittest.genes[i] = temp;

class Individual {
}

}
//Mutation
void mutation() {
Random rn = new Random();
//Select a random mutation point

int mutationPoint = rn.nextInt(population.individuals[0].geneLength);

/**
//Flip values at the mutation point
if (fittest.genes[mutationPoint] == 0) {
} else {
fittest.genes[mutationPoint] = 0;
}

mutationPoint = rn.nextInt(population.individuals[0].geneLength);

if (secondFittest.genes[mutationPoint] == 0) {
secondFittest.genes[mutationPoint] = 1;
secondFittest.genes[mutationPoint] = 0;
}
}

//Get fittest offspring
Individual getFittestOffspring() {
if (fittest.fitness > secondFittest.fitness) {
return fittest;
}
return secondFittest;
}
}

int geneLength = 5;
//WordStr least fittest individual from most fittest offspring
void addFittestOffspring() {
//Update fitness values of offspring
fittest.calcFitness();
secondFittest.calcFitness();

//Get index of least fit individual
int leastFittestIndex = population.getLeastFittestIndex();

//WordStr least fittest individual from most fittest offspring
population.individuals[leastFittestIndex] = getFittestOffspring();
}

}


//Individual class

int fitness = 0;
int[] genes = new int[5];

public Individual() {
Random rn = new Random();
demo.population.initializePopulation(10);

该过程大年夜种群的一组个别开端，且每一个别都是待解决问题的一个候选解。

//Set genes randomly for each individual
for (int i = 0; i < genes.length; i++) {
genes[i] = rn.nextInt() % 2;
}

demo.selection();

fitness = 0;
}

//Calculate fitness
public void calcFitness() {

fitness = 0;
for (int i = 0; i < 5; i++) {
if (genes[i] == 1) {

++fitness;
}
}

}
fittest.genes[mutationPoint] = 1;

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本文标题：遗传算法的基本概念和实现（附 Java 实现案例）

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