path: root/src/jcgp/JCGP.java

package jcgp;

import java.io.File;

import jcgp.backend.modules.es.EvolutionaryStrategy;
import jcgp.backend.modules.es.MuPlusLambda;
import jcgp.backend.modules.es.TournamentSelection;
import jcgp.backend.modules.mutator.Mutator;
import jcgp.backend.modules.mutator.PointMutator;
import jcgp.backend.modules.problem.DigitalCircuitProblem;
import jcgp.backend.modules.problem.Problem;
import jcgp.backend.modules.problem.SymbolicRegressionProblem;
import jcgp.backend.modules.problem.TestCaseProblem;
import jcgp.backend.parser.ChromosomeParser;
import jcgp.backend.parser.FunctionParser;
import jcgp.backend.parser.ParameterParser;
import jcgp.backend.parser.TestCaseParser;
import jcgp.backend.population.Population;
import jcgp.backend.resources.Console;
import jcgp.backend.resources.ModifiableResources;
import jcgp.backend.resources.Resources;

/**
 * 
 * Top-level CGP class. This class is the entry point for a CGP experiment. 
 * <br>
 * An instance of JCGP encapsulates the entire experiment. It contains a Resources
 * object which can be retrieved via a getter. Modules can be selected using their
 * respective setters and function sets can be selected through the resources.
 * 
 * The flow of the experiment is controlled using start() and nextGeneration(). The
 * experiment can be reset with reset(), TODO comment
 * 
 * 
 * @author Eduardo Pedroni
 * @see Resources, Module, FunctionSet
 */
public class JCGP {
	
	// make resources
	private final ModifiableResources resources = new ModifiableResources();
	
	/*
	 * The following arrays contain all available modules. These collections are read by the GUI
	 * when generating menus, so modules not added here will *NOT* be selectable in the GUI.
	 * 
	 * Each array is accompanied by a field which contains a reference to the currently selected
	 * module, 0 by default.
	 */
	// mutators
	private Mutator[] mutators = new Mutator[] {
			new PointMutator(resources) };
	private Mutator mutator;
	
	// evolutionary algorithms
	private EvolutionaryStrategy[] evolutionaryStrategies = new EvolutionaryStrategy[] {
			new MuPlusLambda(resources),
			new TournamentSelection() };
	private EvolutionaryStrategy evolutionaryStrategy;
	
	// problem types
	private Problem[] problems = new Problem[] {
			new SymbolicRegressionProblem(resources),
			new DigitalCircuitProblem(resources) };
	private Problem problem;

	/*
	 * the population of chromosomes
	 */
	private Population population;
	private boolean finished = false;
	
	/**
	 * TODO comment this!
	 * 
	 * @param args
	 */
	public static void main(String... args) {
		if (args.length < 1) {
			System.err.println("JCGP requires at least a .par file.");
			System.exit(1);
		}
		JCGP jcgp = new JCGP();
		jcgp.loadParameters(new File(args[0]));
		
		if (jcgp.getProblem() instanceof TestCaseProblem) {
			((TestCaseProblem<?>) jcgp.getProblem()).parse(new File(args[2]));
		}
		
		jcgp.start();
	}
	
	public JCGP() {
		setEvolutionaryStrategy(0);
		setMutator(0);
		setProblem(0);
		
		population = new Population(resources);
	}

	public ModifiableResources getResources() {
		return resources;
	}
	
	public Population getPopulation() {
		return population;
	}

	/**
	 * @return the mutators
	 */
	public Mutator[] getMutators() {
		return mutators;
	}


	/**
	 * @return the mutator
	 */
	public Mutator getMutator() {
		return mutator;
	}


	/**
	 * @return the evolutionaryAlgorithms
	 */
	public EvolutionaryStrategy[] getEvolutionaryStrategies() {
		return evolutionaryStrategies;
	}


	/**
	 * @return the evolutionaryAlgorithm
	 */
	public EvolutionaryStrategy getEvolutionaryStrategy() {
		return evolutionaryStrategy;
	}


	/**
	 * @return the fitnessFunctions
	 */
	public Problem[] getProblems() {
		return problems;
	}


	/**
	 * @return the fitnessFunction
	 */
	public Problem getProblem() {
		return problem;
	}
	
	
	/**
	 * @param mutator the mutator to set
	 */
	public void setMutator(int index) {
		this.mutator = mutators[index];
	}


	/**
	 * @param evolutionaryStrategy the evolutionaryAlgorithm to set
	 */
	public void setEvolutionaryStrategy(int index) {		
		this.evolutionaryStrategy = evolutionaryStrategies[index];
	}


	/**
	 * @param problem the fitnessFunction to set
	 */
	public void setProblem(int index) {
		this.problem = problems[index];
		resources.setFunctionSet(problem.getFunctionSet());
	}
	
	public void nextGeneration() {
		if (!finished) {
			problem.evaluate(population, (Resources) resources);
			reportGeneration();

			if (resources.currentGeneration() < resources.generations()) {
				// we still have generations left to go
				if (problem.isPerfectSolution(population.getFittest())) {
					// solution has been found, start next run
					resources.println("[CGP] Solution found, generation " + resources.currentGeneration() + ", chromosome " + population.getFittestIndex());
					
					if (resources.currentRun() < resources.runs()) {
						// there are still runs left
						resources.setCurrentRun(resources.currentRun() + 1);
						resources.setCurrentGeneration(0);
						
						// start a new population
						population.reinitialise();
					} else {
						// no more generations and no more runs, we're done
						finished = true;
					}
				} else {
					resources.setCurrentGeneration(resources.currentGeneration() + 1);
				}
			} else {
				// the run has ended, check if any more runs must be done
				resources.println("[CGP] Solution not found, highest fitness achieved was "
						+ population.getFittest().getFitness()
						+ " by chromosome " + population.getFittestIndex());

				if (resources.currentRun() < resources.runs()) {
					// the run has ended but there are still runs left
					resources.setCurrentRun(resources.currentRun() + 1);
					resources.setCurrentGeneration(0);
					
					// start a new population
					population.reinitialise();
				} else {
					// no more generations and no more runs, we're done
					finished = true;
				}
			}
		}

		evolutionaryStrategy.evolve(population, mutator, (Resources) resources);
		
	}
	
	private void reportGeneration() {
		if (resources.reportInterval() > 0) {
			if (resources.currentGeneration() % resources.reportInterval() == 0) {
				resources.println("[CGP] Generation: " + resources.currentGeneration() + ", fittest chromosome (" 
			+ population.getFittestIndex() + ") has fitness: " + population.getFittest().getFitness());
			}
		}
	}

	public void start() {
		if (!finished) {
			while (!finished) {
				nextGeneration();
			}
		}
	}
	
	public void reset() {
		finished = false;
		population = new Population(resources);
		resources.setCurrentGeneration(1);
		resources.setCurrentRun(1);
		resources.println("-----------------------------");
		resources.println("New experiment: " + problem.toString());
	}
	
	public void loadParameters(File file) {
		ParameterParser.parseParameters(file, resources);
		FunctionParser.parseFunctions(file, problem);
		reset();
	}
	
	public void loadTestCases(File file) {
		if (problem instanceof TestCaseProblem) {
			TestCaseParser.parseParameters(file, resources);
			reset();
			((TestCaseProblem<?>) problem).parse(file);
		}
	}
	
	public void loadChromosome(File file) {
		ChromosomeParser.parse(file, population.getChromosome(0), resources);
	}
	
	public void saveChromosome(File file, int chromosomeIndex) {
		ChromosomeParser.save(file, population.getChromosome(chromosomeIndex));
	}

	public boolean isFinished() {
		return finished;
	}
	
	public void setConsole(Console console) {
		resources.setConsole(console);
	}
}