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package main;
import org.apache.log4j.Logger;
import org.jgap.InvalidConfigurationException;
import org.jgap.event.GeneticEvent;
import org.jgap.event.GeneticEventListener;
import org.jgap.gp.CommandGene;
import org.jgap.gp.GPFitnessFunction;
import org.jgap.gp.GPProblem;
import org.jgap.gp.IGPProgram;
import org.jgap.gp.function.Add;
import org.jgap.gp.function.Multiply;
import org.jgap.gp.function.ReadTerminal;
import org.jgap.gp.function.Subtract;
import org.jgap.gp.impl.DeltaGPFitnessEvaluator;
import org.jgap.gp.impl.GPConfiguration;
import org.jgap.gp.impl.GPGenotype;
import org.jgap.gp.impl.TournamentSelector;
import org.jgap.gp.terminal.Variable;
import org.jgap.util.SystemKit;
public class Main
extends GPProblem
{
private transient static Logger LOGGER = Logger.getLogger(Main.class);
static Variable va;
static Variable vb;
static Variable vc;
static Variable vy;
static final int NUMBER=50;
static int initData[][]=new int[NUMBER][4];
public Main(GPConfiguration config) throws InvalidConfigurationException
{
super(config);
}
public static void main(String[] args) throws Exception
{
System.out.println("Program to discover: a+b-c");
// configuration standard pour un GP
GPConfiguration config = new GPConfiguration();
// Une evaluateur de fitness qui prends en entree le delta des
// valeurs. Plus le delta est petit plus le fitness est bon.
// TODO: voir les autres Fitness, [l'influence des parametres]
config.setGPFitnessEvaluator(new DeltaGPFitnessEvaluator());
// Un tournoi de selection pour GP. Le gagnant est determine
// en faisant s'opposer le nombre en parametre d'opposant entre
// eux.
// TODO: voir les autres Selector, l'influence des parametres
config.setSelectionMethod(new TournamentSelector(4));
// Taille de la population
int popSize = 200;
System.out.println("Using population size of " + popSize);
// SUPPOSE: profondeur maximale d'une expresion a l'initialisation
config.setMaxInitDepth(10);
// Affecte la taille de la population
config.setPopulationSize(popSize);
// Definit la fonction fitness qui est contenu dans une classe
// heritant de GPFitnessFunction s'appelle evaluate(IGPProgram):
// double
//
// La classe Formula.. est incluse dans Main.
/*
* Dans notre cas on veut decouvrir "a+b-c" donc il faut 3
* variables avec un jeux de données et Deux opérateurs.
* On applique l'algorithme trouver qui retourne le delta entre
* la y=a+b-c et y' de GP.
*/
config.setFitnessFunction(new Main.FormulaFitnessFunction());
// Si true declenche une exception si une function ou un terminal
// d'un certain type est requis n'est pas disponible.
config.setStrictProgramCreation(false);
// ?? Pas documenter
config.setProgramCreationMaxTries(3);
// ?? Pas documenter
config.setMaxCrossoverDepth(5);
config.setReproductionProb(1f);
config.setMutationProb(1f);
// Set a node validator to demonstrate speedup when something is known
// about the solution (see FibonacciNodeValidator).
// -------------------------------------------------------------------
/* config.setNodeValidator(new FibonacciNodeValidator()); */
// Ative le cache des programmes GP => Les valeurs de fitness seront
// mis en cache pour tous les evolutions de programmes équivalents
// aux anciens.
config.setUseProgramCache(true);
// creer la classe probleme a partir de la config
final GPProblem problem = new Main(config);
// Creer un genotype a partir de la config.
GPGenotype gp = problem.create();
// ??
gp.setVerboseOutput(true);
// lance le traitement par thread
final Thread t = new Thread(gp);
/*
* On implémente le run après le lancement du thread. Etrange.
*/
// Simple implementation of running evolution in a thread.
// -------------------------------------------------------
config
.getEventManager()
.addEventListener(
GeneticEvent.
GPGENOTYPE_EVOLVED_EVENT,
new GeneticEventListener()
{
@SuppressWarnings({ "deprecation", "static-access" })
public void geneticEventFired
(GeneticEvent a_firedEvent)
{
GPGenotype genotype = (GPGenotype)
a_firedEvent.getSource();
int evno = genotype .getGPConfiguration()
.getGenerationNr();
double freeMem = SystemKit.getFreeMemoryMB();
if (evno % 50 == 0)
{
IGPProgram best = genotype.getAllTimeBest();
double allBestFitness = best.getFitnessValue();
LOGGER.info("Evolving generation " + evno
+ ", all-time-best fitness: "
+ allBestFitness
+ ", memory free: "
+ freeMem + " MB");
genotype.outputSolution(best);
}
if (evno > 3000)
{
t.stop();
}
else
{
try {
// Collect garbage if memory low.
// ------------------------------
if (freeMem < 50)
{
System.gc();
t.sleep(500);
}
else
{
// Avoid 100% CPU load.
// --------------------
t.sleep(30);
}
}
catch (InterruptedException iex)
{
iex.printStackTrace();
System.exit(1);
}
}
}
});
config.getEventManager().addEventListener(GeneticEvent.
GPGENOTYPE_NEW_BEST_SOLUTION, new GeneticEventListener()
{
/**
* New best solution found.
*
* @param a_firedEvent GeneticEvent
*/
@SuppressWarnings("deprecation")
public void geneticEventFired(GeneticEvent a_firedEvent) {
GPGenotype genotype = (GPGenotype) a_firedEvent.getSource();
int evno = genotype.getGPConfiguration().getGenerationNr();
String indexString = "" + evno;
while (indexString.length() < 5) {
indexString = "0" + indexString;
}
String filename = "function_" + indexString + ".png";
IGPProgram best = genotype.getAllTimeBest();
try {
problem.showTree(best, filename);
} catch (InvalidConfigurationException iex) {
iex.printStackTrace();
}
double bestFitness = genotype.getFittestProgram().
getFitnessValue();
if (bestFitness < 0.001) {
genotype.outputSolution(best);
t.stop();
System.exit(0);
}
}
});
t.start();
}
public static class FormulaFitnessFunction
extends GPFitnessFunction
{
private static final long serialVersionUID = 1L;
protected double evaluate(final IGPProgram a_subject)
{
return computeRawFitness(a_subject);
}
}
public static double computeRawFitness(final IGPProgram a_program)
{
Object[] noargs = new Object[0];
// On vide la pile et la memoire du programme
// ------------------------
a_program.getGPConfiguration().clearStack();
a_program.getGPConfiguration().clearMemory();
// On calcule son fitness
// ---------------------------------
double result = 0.0d;
for(int i=0;i<NUMBER;i++)
{
va.set(initData[i][0]);
vb.set(initData[i][1]);
vc.set(initData[i][2]);
// On execute dans l'ordre chaque instruction du programme.
try {
try {
// Quand on arrive a la fin des instructions du
// programmes on recupere le resultat finale comme
// resultat finale
// -------------------------------------------------------------
result += a_program.execute_int(0, noargs);
return Math.abs(initData[i][3]-result);
}
catch (IllegalStateException iex)
{
result = GPFitnessFunction.MAX_FITNESS_VALUE;
}
}
catch (ArithmeticException ex)
{
System.out.println("(y,result) = (" +initData[i][3]+","+result+")");
System.out.println(a_program.getChromosome(0));
throw ex;
}
}
if (a_program.getGPConfiguration().stackSize() > 0) {
result = GPFitnessFunction.MAX_FITNESS_VALUE;
}
if (result < 0.000001) {
result = 0.0d;
}
else if (result < GPFitnessFunction.MAX_FITNESS_VALUE) {
/**@todo add penalty for longer solutions*/
}
return result;
}
/**
* Sets up the functions to use and other parameters. Then creates the
* initial genotype.
*
* @return the genotype created
* @throws InvalidConfigurationException
*
* @author Klaus Meffert
* @since 3.0
*/
@SuppressWarnings("unchecked")
public GPGenotype create()
throws InvalidConfigurationException {
// Define return types of sub programs (see nodeSets).
// The first entry in types corresponds with the first entry in nodeSets,
// etc.
Class[] types ={
CommandGene.IntegerClass
};
// The following is only relevant for ADF's and not used here.
Class[][] argTypes = { {} };
// Configure desired minimum number of nodes per sub program.
// Same as with types: First entry here corresponds with first entry in
// nodeSets.
int[] minDepths = new int[] {5};
// Configure desired maximum number of nodes per sub program.
// First entry here corresponds with first entry in nodeSets.
int[] maxDepths = new int[] {10};
GPConfiguration conf = getGPConfiguration();
/**@todo allow to optionally preset a static program in each chromosome*/
/*
* Il s'agit d'un tableau d'operateur a 2 dimensions. La premiere est
* le chromosome dans sa longeur. La deuxieme sont les different
* operateurs possible pour cette allele du chromosome.
*/
va = Variable.create(conf, "A", CommandGene.IntegerClass);
vb = Variable.create(conf, "B", CommandGene.IntegerClass);
vc = Variable.create(conf, "C", CommandGene.IntegerClass);
vy = Variable.create(conf, "C", CommandGene.IntegerClass);
CommandGene[][] nodeSets =
{
{
new ReadTerminal (conf,CommandGene.IntegerClass, "A"),
new ReadTerminal (conf,CommandGene.IntegerClass, "B"),
new ReadTerminal (conf,CommandGene.IntegerClass, "C"),
new ReadTerminal (conf,CommandGene.IntegerClass, "Y"),
new Add (conf,CommandGene.IntegerClass),
new Subtract (conf,CommandGene.IntegerClass),
new Multiply (conf,CommandGene.IntegerClass)
}
};
// Add commands working with internal memory.
// ------------------------------------------
/*
nodeSets[2] = CommandFactory.createReadOnlyCommands(nodeSets[2],
conf,
CommandGene.IntegerClass, "mem", 1, 2, !true);
*/
// Randomly initialize function data (X-Y table) for Fib(x).
// ---------------------------------------------------------
for(int i=0;i<NUMBER;i++)
{
initData[i][0]=(int)(Math.random()*Integer.MAX_VALUE);
initData[i][1]=(int)(Math.random()*Integer.MAX_VALUE);
initData[i][2]=(int)(Math.random()*Integer.MAX_VALUE);
initData[i][3]=initData[i][0]+initData[i][1]-initData[i][2];
}
// Create genotype with initial population.
// ----------------------------------------
// new boolean[] {!true, !true, false}
// a_fullModeAllowed - array of boolean values.
// For each chromosome there is one value indicating
// whether the full mode for creating chromosome generations
// during evolution is allowed (true) or not (false)
return GPGenotype.randomInitialGenotype(
conf, types, argTypes, nodeSets,
minDepths, maxDepths, 20,
new boolean[] {true},
// new boolean[] {!true, !true, !true, false},
// new boolean[] {true, true, true, !false},
true);
}
} |
