parallelga.cpp

/********************************************************************************
 *  FARSA Genetic Algorithm Library                                             *
 *  Copyright (C) 2007-2009 Gianluca Massera <emmegian@yahoo.it>                *
 *                                                                              *
 *  This program is free software; you can redistribute it and/or modify        *
 *  it under the terms of the GNU General Public License as published by        *
 *  the Free Software Foundation; either version 2 of the License, or           *
 *  (at your option) any later version.                                         *
 *                                                                              *
 *  This program is distributed in the hope that it will be useful,             *
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of              *
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               *
 *  GNU General Public License for more details.                                *
 *                                                                              *
 *  You should have received a copy of the GNU General Public License           *
 *  along with this program; if not, write to the Free Software                 *
 *  Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA  *
 ********************************************************************************/

#include "factory.h"
#include "gas/parallelga.h"
#include "core/reproduction.h"
#include "core/genome.h"
#include "core/evaluation.h"
#include <QThreadPool>
#include <QtConcurrentMap>
using namespace QtConcurrent;

namespace farsa {

ParallelGA::ParallelGA()
    : GeneticAlgo() {
    fitfunc = 0;
    reprod = 0;
    numGens = 0;
    currPhase = initEvaluation;
    numThreadv = 1;
    isInitialized = false;
    isFinalized = true;
    future = new QFuture<void>();
}

ParallelGA::~ParallelGA() {
    foreach( ParallelGA::evaluationThread* e, evalThreads ) {
        delete e;
    }
    delete fitfunc;
}

void ParallelGA::configure( ConfigurationParameters& params, QString prefix ) {
    setGenome( params.getObjectFromGroup<Genome>( prefix + QString( "GENOME" ) ) );
    setEvaluation( params.getObjectFromGroup<Evaluation>( prefix + QString( "EVALUATION" ) ) );
    fitfunc->setGenome( genome() );
    setReproduction( params.getObjectFromGroup<Reproduction>( prefix + QString( "REPRODUCTION" ) ) );
    setNumGenerations( params.getValue( prefix + QString( "ngenerations" ) ).toInt() );
    if ( numGenerations() == 0 ) {
        qWarning( "Setting number of Generations to ZERO!! Check your config file" );
    }
    setNumThreads( params.getValue( prefix + QString( "numThreads" ) ).toInt() );
}

void ParallelGA::save( ConfigurationParameters& params, QString prefix ) {
    params.createParameter( prefix, QString("type"), "ParallelGA" );
    params.createParameter( prefix, QString("numThreads"), QString("%1").arg( numThreads() ) );
    params.createParameter( prefix, QString("ngenerations"), QString("%1").arg( numGenerations() ) );
    //--- EVALUATION
    fitfunc->save( params, params.createSubGroup( prefix, "EVALUATION" ) );
    //--- REPRODUCTION
    reproduction()->save( params, params.createSubGroup( prefix, "REPRODUCTION" ) );
    //--- GENOME
    genome()->save( params, params.createSubGroup( prefix, "GENOME" ) );
}

void ParallelGA::describe( QString type ) {
    Descriptor d = addTypeDescription( type, "Parallel Genetic Algorithm", "Respect to SimpleGA and others type of Genetic Algorithm, the implementation of the parallelization is more efficient" );
    d.describeInt( "numThreads" ).limits( 1, 32 ).help( "Number of threads to parallelize the evaluation of individuals" );
    d.describeInt( "ngenerations" ).limits( 1, INT_MAX ).def( 1000 ).help( "Number of the generations of the evolutionary process" );
    d.describeSubgroup( "EVALUATION" ).type( "Evaluation" ).props( IsMandatory ).help( "Object that calculate the fitness", "Create a subclass of Evalution and code your custom fitness function" );
    d.describeSubgroup( "REPRODUCTION").type( "Reproduction" ).props( IsMandatory ).help( "Object that generate the new generations" );
    d.describeSubgroup( "GENOME" ).type( "Genome" ).props( IsMandatory ).help( "Object containing the individuals under evolution" );
}

void ParallelGA::setNumThreads( int numThreads ) {
    if ( numThreads < 1 ) {
        qWarning( "The number of Threads must be greater than one!!" );
    }
    Q_ASSERT_X( !isInitialized && isFinalized ,
            "ParallelGA::setNumThreads",
            "This method can only called before initialize of ParallelGA" );
    Q_ASSERT_X( fitfunc != 0 ,
            "ParallelGA::setNumThreads",
            "This method must be called after an Evaluation object has been setted by ParallelGA::setEvaluation" );
    numThreadv = numThreads;
    return;
}

int ParallelGA::numThreads() {
    return numThreadv;
}

void ParallelGA::setEvaluation( Evaluation* fitfunc ) {
    this->fitfunc = fitfunc;
    this->fitfunc->setGA( this );
}

Evaluation* ParallelGA::evaluationPrototype()
{
    return fitfunc;
}

QVector<Evaluation*> ParallelGA::evaluationPool() {
    QVector<Evaluation*> ev;
    foreach( ParallelGA::evaluationThread* e, evalThreads ) {
        ev.append( e->eval );
    }
    return ev;
}

void ParallelGA::setReproduction( Reproduction* reprod ) {
    this->reprod = reprod;
    this->reprod->setGA( this );
}

Reproduction* ParallelGA::reproduction() {
    return reprod;
}

void ParallelGA::initialize() {
    if ( isInitialized && !isFinalized ) return;
    Q_ASSERT_X( fitfunc != 0 ,
            "ParallelGA::initialize",
            "You have to setup the Evaluation object of ParallelGA (Fitness Function)" );
    Q_ASSERT_X( reprod !=0 ,
            "ParallelGA::initialize",
            "You have to setup the Reproduction operator of ParallelGA" );

    isInitialized = true;
    isFinalized = false;
    setGeneration( 0 );
    currPhase = initEvaluation;
    evolutionEnd = false;
    evaluationDone = false;
    //--- Setting up the evalThreads
    for( int i=0; i<evalThreads.size(); i++ ) {
        delete (evalThreads[i]);
    }
    evalThreads.clear();
    for( int i=0; i<numThreadv; i++ ) {
        evalThreads.append( new evaluationThread( fitfunc ) );
    }
    //--- set the number of thread to create
    QThreadPool::globalInstance()->setMaxThreadCount( numThreadv );
}

void ParallelGA::gaStep() {
    switch( currPhase ) {
    case initEvaluation:
        for( int i=0; i<numThreadv; i++ ) {
            evalThreads[i]->sequence.clear();
        }
        for( int i=0; i<(int)genome()->size(); i++ ) {
            evalThreads[ i%numThreadv ]->sequence.append( i );
        }
        for( int i=0; i<numThreadv; i++ ) {
            evalThreads[i]->idSeq = 0;
            evalThreads[i]->id = evalThreads[i]->sequence[ evalThreads[i]->idSeq ];
            evalThreads[i]->eval->setGenome( genome() );
        }
        currPhase = evaluating;
        //--- here also starts to run all sub-threads for evaluation
        //--- it evaluate all genotypes of the population in separate threads
        (*future) = map( evalThreads, ParallelGA::runStepWrapper );
        break;
    case evaluating: /* Multi Thread Block (i.e. Parallel Evaluation of Genotypes */
        //--- check if the evaluation has been completed
        if ( future->isFinished() ) {
            currPhase = nextGeneration_pass1;
        }
        break;
    case nextGeneration_pass1:
        qSort( genome()->begin(), genome()->end(), Genotype::rankGreaterThanComparator );
        updateStats();
        evaluationDone = true;
        if ( generation() < numGens ) {
            currPhase = nextGeneration_pass2;
        } else {
            currPhase = endEvolution;
        }
        break;
    case nextGeneration_pass2: {
        //--- this additional pass is for avoid to modify the
        //--- genotypes contained in the last generation of the evolution
        //--- and to allow to save the genotypes after each generation
        Genome* old = genome();
        setGenome( reprod->reproduction( old ) );
        delete old;
        fitfunc->setGenome( genome() );
        evaluationDone = false;
        setGeneration( generation()+1 );
        currPhase = initEvaluation;
        }
        break;
    case endEvolution:
        finalize();
        break;
    default:
        qFatal( "Default switch in ParallelGA::gaStep" );
        break;
    }
}

void ParallelGA::skipEvaluation() {
    // Set evaluation done, and check which phase to go to
    evaluationDone = true;
    if ( generation() < numGens ) {
        currPhase = nextGeneration_pass2;
    } else {
        currPhase = endEvolution;
    }
}

void ParallelGA::finalize() {
    if ( isFinalized && !isInitialized ) return;

    isInitialized = false;
    isFinalized = true;
    evolutionEnd = true;
}

ParallelGA::evaluationThread::evaluationThread( Evaluation* eProto )
    : id(0) {
    eval = eProto->clone();
    eval->setGenome( eProto->GA()->genome() );
    eval->setGA( eProto->GA() );
}

ParallelGA::evaluationThread::~evaluationThread() {
    delete eval;
    sequence.clear();
}

void ParallelGA::evaluationThread::runStep() {
    //--- it evaluate all individual assigned to this thread
    while( true ) {
        eval->initialize( eval->GA()->genome()->at( id ) );
        eval->evaluate();
        int nextIdSeq = idSeq + 1;
        eval->finalize();
        eval->genotype()->setRank( eval->genotype()->fitness() );
        if ( nextIdSeq >= sequence.size() ) {
            return;
        }
        idSeq = nextIdSeq;
        int nextId = sequence[ idSeq ];
        id = nextId;
    }
}

void ParallelGA::runStepWrapper( ParallelGA::evaluationThread* e ) {
    e->runStep();
}

} // end namespace farsa