遗传算法代码实现

import java.util.Random;
public class SimpleDemoGA {


Population population = new Population();
    Individual fittest;
    Individual secondFittest;
    int generationCount = 0;


    public static void main(String[] args) {


        Random rn = new Random();
        
        SimpleDemoGA demo = new SimpleDemoGA();
        
        //Initialize population
        demo.population.initializePopulation(10);
        
        //Calculate fitness of each individual
        demo.population.calculateFitness();
        
        System.out.println("Generation: " + demo.generationCount + " Fittest: " + demo.population.fittest);


        //While population gets an individual with maximum fitness
        while (demo.population.fittest < 5) {
            ++demo.generationCount;
            
            //Do selection
            demo.selection();
            
            //Do crossover
            demo.crossover();
            
            //Do mutation under a random probability
            if (rn.nextInt()%7 < 5) {
                demo.mutation();
            }
            
            //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("");


    }


    //Selection
    void selection() {
        
        //Select the most fittest individual
        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;


        }


    }


    //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) {
            fittest.genes[mutationPoint] = 1;
        } else {
            fittest.genes[mutationPoint] = 0;
        }


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


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


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




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


}




//Individual class
class Individual {


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


    public Individual() {
        Random rn = new Random();


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


        fitness = 0;
    }


    //Calculate fitness
    public void calcFitness() {


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


}


//Population class
class Population {


    int popSize = 10;
    Individual[] individuals = new Individual[10];
    int fittest = 0;


    //Initialize population
    public void initializePopulation(int size) {
        for (int i = 0; i < individuals.length; i++) {
            individuals[i] = new Individual();
        }
    }


    //Get the fittest individual
    public Individual getFittest() {
        int maxFit = Integer.MIN_VALUE;
        for (int i = 0; i < individuals.length; i++) {
            if (maxFit <= individuals[i].fitness) {
                maxFit = i;
            }
        }
        fittest = individuals[maxFit].fitness;
        return individuals[maxFit];
    }


    //Get the second most fittest individual
    public Individual getSecondFittest() {
        int maxFit1 = 0;
        int maxFit2 = 0;
        for (int i = 0; i < individuals.length; i++) {
            if (individuals[i].fitness > individuals[maxFit1].fitness) {
                maxFit2 = maxFit1;
                maxFit1 = i;
            } else if (individuals[i].fitness > individuals[maxFit2].fitness) {
                maxFit2 = i;
            }
        }
        return individuals[maxFit2];
    }


    //Get index of least fittest individual
    public int getLeastFittestIndex() {
        int minFit = 0;
        for (int i = 0; i < individuals.length; i++) {
            if (minFit >= individuals[i].fitness) {
                minFit = i;
            }
        }
        return minFit;
    }


    //Calculate fitness of each individual
    public void calculateFitness() {


        for (int i = 0; i < individuals.length; i++) {
            individuals[i].calcFitness();
        }
        getFittest();
    }


}