package jcgp.modules.ea;

import jcgp.modules.mutator.Mutator;
import jcgp.parameters.Parameters;
import jcgp.parameters.IntegerParameter;
import jcgp.parameters.BooleanParameter;
import jcgp.population.Chromosome;
import jcgp.population.Population;

/**
 * (1 + λ) EA.
 * 
 * 
 * @author Eduardo Pedroni
 *
 */
public class StandardEA implements EvolutionaryAlgorithm {
	
	private Chromosome fittestChromosome;

	@Override
	public void evolve(Population population, Mutator mutator) {
		// select fittest chromosome
		int fittest = 0;
		
		for (int i = 1; i < ((IntegerParameter) Parameters.get("population")).getValue(); i++) {
			if (population.getChromosome(i).getFitness() >= population.getChromosome(fittest).getFitness()) {
				fittest = i;
			}
		}
		fittestChromosome = population.getChromosome(fittest);
		population.setBestIndividual(fittest);
		if (((BooleanParameter) Parameters.get("debug")).getValue()) {
			System.out.println("Best fitness: " + fittestChromosome.getFitness());
		}
		// create copies of fittest chromosome, mutate them
		Chromosome fc = population.getChromosome(fittest);
		for (int i = 0; i < ((IntegerParameter) Parameters.get("population")).getValue(); i++) {
			if (i != fittest) {
				population.getChromosome(i).copyConnections(fc);
				mutator.mutate(population.getChromosome(i));
			}
		}
	}

	@Override
	public Chromosome getFittestChromosome() {
		return fittestChromosome;
	}
}