遗传算法AllOnesGA

package chapter2;

/**
 * This is our main class used to run the genetic algorithm.
 * 
 * This case is one of the simplest problems we can solve: the objective is to
 * end up with an individual whose chromosome is all ones.
 * 
 * The simplicity of this problem makes the GeneticAlgorithm class‘
 * "calcFitness" method very simple. We‘ll just count the number of ones in the
 * chromosome and use that as the fitness score. Similarly, the
 * "isTerminationConditionMet" method in the GeneticAlgorithm class for this
 * example is very simple: if the fitness score (ie, number of ones) is the same
 * as the length of the chromosome (ie, we‘re all ones), we‘re done!
 * 
 * @author bkanber
 *
 */
public class AllOnesGA {

    public static void main(String[] args) {
        // Create GA object
        GeneticAlgorithm ga = new GeneticAlgorithm(100, 0.001, 0.95, 2);

        // Initialize population
        Population population = ga.initPopulation(50);

        // Evaluate population
        ga.evalPopulation(population);

        // Keep track of current generation
        int generation = 1;

        /**
         * Start the evolution loop
         * 
         * Every genetic algorithm problem has different criteria for finishing.
         * In this case, we know what a perfect solution looks like (we don‘t
         * always!), so our isTerminationConditionMet method is very
         * straightforward: if there‘s a member of the population whose
         * chromosome is all ones, we‘re done!
         */
        while (ga.isTerminationConditionMet(population) == false) {
            // Print fittest individual from population
            System.out.println("Best solution: " + population.getFittest(0).toString());

            // Apply crossover
            population = ga.crossoverPopulation(population);

            // Apply mutation
            population = ga.mutatePopulation(population);

            // Evaluate population
            ga.evalPopulation(population);

            // Increment the current generation
            generation++;
        }

        /**
         * We‘re out of the loop now, which means we have a perfect solution on
         * our hands. Let‘s print it out to confirm that it is actually all
         * ones, as promised.
         */
        System.out.println("Found solution in " + generation + " generations");
        System.out.println("Best solution: " + population.getFittest(0).toString());
    }
}