STD_MEM.tpl 19 KB
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\TEMPLATE_START
#ifdef WIN32
#define _CRT_SECURE_NO_WARNINGS
#pragma comment(lib, "libEasea.lib")
#pragma comment(lib, "Winmm.lib")
#endif
/**
 This is program entry for STD template for EASEA

*/

\ANALYSE_PARAMETERS
#include <stdlib.h>
#include <iostream>
#include <time.h>
#include "COptionParser.h"
#include "CRandomGenerator.h"
#include "CEvolutionaryAlgorithm.h"
#include "global.h"
#include "EASEAIndividual.hpp"

using namespace std;

/** Global variables for the whole algorithm */
CIndividual** pPopulation = NULL;
CIndividual*  bBest = NULL;
float* pEZ_MUT_PROB = NULL;
float* pEZ_XOVER_PROB = NULL;
size_t *EZ_NB_GEN;
size_t *EZ_current_generation;
CEvolutionaryAlgorithm* EA;

int main(int argc, char** argv){


	parseArguments("EASEA.prm",argc,argv);

	ParametersImpl p;
	p.setDefaultParameters(argc,argv);
	CEvolutionaryAlgorithm* ea = p.newEvolutionaryAlgorithm();

	EA = ea;

	EASEAInit(argc,argv);

	CPopulation* pop = ea->getPopulation();

	ea->runEvolutionaryLoop();

	EASEAFinal(pop);

	delete pop;


#ifdef WIN32
	system("pause");
#endif
	return 0;
}

\START_CUDA_GENOME_CU_TPL
#ifdef WIN32
#define _CRT_SECURE_NO_WARNINGS
#pragma comment(lib, "libEasea.lib")
#endif

#include <string.h>
#include <fstream>
#ifndef WIN32
#include <sys/time.h>
#else
#include <time.h>
#endif
#include "CRandomGenerator.h"
#include "CPopulation.h"
#include "COptionParser.h"
#include "CStoppingCriterion.h"
#include "CEvolutionaryAlgorithm.h"
#include "global.h"
#include "CIndividual.h"

using namespace std;

#include "EASEAIndividual.hpp"
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bool INSTEAD_EVAL_STEP = false;
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CRandomGenerator* globalRandomGenerator;
extern CEvolutionaryAlgorithm* EA;
#define STD_TPL

\INSERT_USER_DECLARATIONS
\ANALYSE_USER_CLASSES

\INSERT_USER_CLASSES

\INSERT_USER_FUNCTIONS

\INSERT_INITIALISATION_FUNCTION
\INSERT_FINALIZATION_FUNCTION

\INSERT_BOUND_CHECKING

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void evale_pop_chunk(CIndividual** population, int popSize){
  \INSTEAD_EVAL_FUNCTION
}

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void EASEAInit(int argc, char** argv){
	\INSERT_INIT_FCT_CALL
}

void EASEAFinal(CPopulation* pop){
	\INSERT_FINALIZATION_FCT_CALL;
}

void AESAEBeginningGenerationFunction(CEvolutionaryAlgorithm* evolutionaryAlgorithm){
	\INSERT_BEGIN_GENERATION_FUNCTION
}

void AESAEEndGenerationFunction(CEvolutionaryAlgorithm* evolutionaryAlgorithm){
	\INSERT_END_GENERATION_FUNCTION
}

void AESAEGenerationFunctionBeforeReplacement(CEvolutionaryAlgorithm* evolutionaryAlgorithm){
	\INSERT_GENERATION_FUNCTION_BEFORE_REPLACEMENT
}


IndividualImpl::IndividualImpl() : CIndividual() {
  \GENOME_CTOR 
  \INSERT_EO_INITIALISER
  valid = false;
}

CIndividual* IndividualImpl::clone(){
	return new IndividualImpl(*this);
}

IndividualImpl::~IndividualImpl(){
  \GENOME_DTOR
}


float IndividualImpl::evaluate(){
    valid = true;
    \INSERT_EVALUATOR
}

IndividualImpl::IndividualImpl(const IndividualImpl& genome){

  // ********************
  // Problem specific part
  \COPY_CTOR


  // ********************
  // Generic part
  this->valid = genome.valid;
  this->fitness = genome.fitness;
}

CIndividual* IndividualImpl::crossover(CIndividual** ps){
	// ********************
	// Generic part
	IndividualImpl** tmp = (IndividualImpl**)ps;
	IndividualImpl parent1(*this);
	IndividualImpl parent2(*tmp[0]);
	IndividualImpl child(*this);

	//DEBUG_PRT("Xover");
	/*   cout << "p1 : " << parent1 << endl; */
	/*   cout << "p2 : " << parent2 << endl; */

	// ********************
	// Problem specific part
  	\INSERT_CROSSOVER


	child.valid = false;
	/*   cout << "child : " << child << endl; */
	return new IndividualImpl(child);
}


void IndividualImpl::printOn(std::ostream& os) const{
	\INSERT_DISPLAY
}

std::ostream& operator << (std::ostream& O, const IndividualImpl& B)
{
  // ********************
  // Problem specific part
  O << "\nIndividualImpl : "<< std::endl;
  O << "\t\t\t";
  B.printOn(O);

  if( B.valid ) O << "\t\t\tfitness : " << B.fitness;
  else O << "fitness is not yet computed" << std::endl;
  return O;
}


size_t IndividualImpl::mutate( float pMutationPerGene ){
  this->valid=false;


  // ********************
  // Problem specific part
  \INSERT_MUTATOR
}

void IndividualImpl::optimiser(int currentIteration){
  \INSERT_OPTIMISER	
}

void PopulationImpl::optimisePopulation(CIndividual** population, size_t populationSize){
	for(int iter=0; iter<params->optimiseIterations; iter++){
		for(int i=0; i<(signed)populationSize; i++){
			IndividualImpl* tmp = new IndividualImpl(*(IndividualImpl*)population[i]);
			tmp->optimiser(iter);
			tmp->evaluate();
			if(params->baldwinism){
				if((\MINIMAXI && tmp->fitness<population[i]->fitness) || (!\MINIMAXI && tmp->fitness>population[i]->fitness))
					population[i]->fitness = tmp->fitness;
				delete tmp;
			}
			else{
				if((\MINIMAXI && tmp->fitness<population[i]->fitness) || (!\MINIMAXI && tmp->fitness>population[i]->fitness)){
					delete population[i];
					population[i]=tmp;
				}
				else
					delete tmp;
			}
		}
	}	
}



void ParametersImpl::setDefaultParameters(int argc, char** argv){

	this->minimizing = \MINIMAXI;
	this->nbGen = setVariable("nbGen",(int)\NB_GEN);

	selectionOperator = getSelectionOperator(setVariable("selectionOperator","\SELECTOR_OPERATOR"), this->minimizing, globalRandomGenerator);
	replacementOperator = getSelectionOperator(setVariable("reduceFinalOperator","\RED_FINAL_OPERATOR"),this->minimizing, globalRandomGenerator);
	parentReductionOperator = getSelectionOperator(setVariable("reduceParentsOperator","\RED_PAR_OPERATOR"),this->minimizing, globalRandomGenerator);
	offspringReductionOperator = getSelectionOperator(setVariable("reduceOffspringOperator","\RED_OFF_OPERATOR"),this->minimizing, globalRandomGenerator);
	selectionPressure = setVariable("selectionPressure",(float)\SELECT_PRM);
	replacementPressure = setVariable("reduceFinalPressure",(float)\RED_FINAL_PRM);
	parentReductionPressure = setVariable("reduceParentsPressure",(float)\RED_PAR_PRM);
	offspringReductionPressure = setVariable("reduceOffspringPressure",(float)\RED_OFF_PRM);
	pCrossover = \XOVER_PROB;
	pMutation = \MUT_PROB;
	pMutationPerGene = 0.05;

	parentPopulationSize = setVariable("popSize",(int)\POP_SIZE);
	offspringPopulationSize = setVariable("nbOffspring",(int)\OFF_SIZE);


	parentReductionSize = setReductionSizes(parentPopulationSize, setVariable("survivingParents",(float)\SURV_PAR_SIZE));
	offspringReductionSize = setReductionSizes(offspringPopulationSize, setVariable("survivingOffspring",(float)\SURV_OFF_SIZE));

	this->elitSize = setVariable("elite",(int)\ELITE_SIZE);
	this->strongElitism = setVariable("eliteType",(int)\ELITISM);

	if((this->parentReductionSize + this->offspringReductionSize) < this->parentPopulationSize){
		printf("*WARNING* parentReductionSize + offspringReductionSize < parentPopulationSize\n");
		printf("*WARNING* change Sizes in .prm or .ez\n");
		printf("EXITING\n");
		exit(1);	
	} 
	if((this->parentPopulationSize-this->parentReductionSize)>this->parentPopulationSize-this->elitSize){
		printf("*WARNING* parentPopulationSize - parentReductionSize > parentPopulationSize - elitSize\n");
		printf("*WARNING* change Sizes in .prm or .ez\n");
		printf("EXITING\n");
		exit(1);	
	} 
	if(!this->strongElitism && ((this->offspringPopulationSize - this->offspringReductionSize)>this->offspringPopulationSize-this->elitSize)){
		printf("*WARNING* offspringPopulationSize - offspringReductionSize > offspringPopulationSize - elitSize\n");
		printf("*WARNING* change Sizes in .prm or .ez\n");
		printf("EXITING\n");
		exit(1);	
	} 
	

	/*
	 * The reduction is set to true if reductionSize (parent or offspring) is set to a size less than the
	 * populationSize. The reduction size is set to populationSize by default
	 */
	if(offspringReductionSize<offspringPopulationSize) offspringReduction = true;
	else offspringReduction = false;

	if(parentReductionSize<parentPopulationSize) parentReduction = true;
	else parentReduction = false;

	cout << "Parent red " << parentReduction << " " << parentReductionSize << "/"<< parentPopulationSize << endl;
	cout << "Parent red " << offspringReduction << " " << offspringReductionSize << "/" << offspringPopulationSize << endl;

	generationalCriterion = new CGenerationalCriterion(setVariable("nbGen",(int)\NB_GEN));
	controlCStopingCriterion = new CControlCStopingCriterion();
	timeCriterion = new CTimeCriterion(setVariable("timeLimit",\TIME_LIMIT));
	
	this->optimise=1;
	this->optimiseIterations = setVariable("optimiseIterations",(int)\NB_OPT_IT);
	this->baldwinism = setVariable("baldwinism",(int)\BALDWINISM);

	seed = setVariable("seed",(int)time(0));
	globalRandomGenerator = new CRandomGenerator(seed);
	this->randomGenerator = globalRandomGenerator;

	this->printStats = setVariable("printStats",\PRINT_STATS);
	this->generateCSVFile = setVariable("generateCSVFile",\GENERATE_CSV_FILE);
	this->generateGnuplotScript = setVariable("generateGnuplotScript",\GENERATE_GNUPLOT_SCRIPT);
	this->generateRScript = setVariable("generateRScript",\GENERATE_R_SCRIPT);
	this->plotStats = setVariable("plotStats",\PLOT_STATS);
	this->printInitialPopulation = setVariable("printInitialPopulation",0);
	this->printFinalPopulation = setVariable("printFinalPopulation",0);

	this->outputFilename = (char*)"EASEA";
	this->plotOutputFilename = (char*)"EASEA.png";
}

CEvolutionaryAlgorithm* ParametersImpl::newEvolutionaryAlgorithm(){

	pEZ_MUT_PROB = &pMutationPerGene;
	pEZ_XOVER_PROB = &pCrossover;
	EZ_NB_GEN = (size_t*)setVariable("nbGen",\NB_GEN);
	EZ_current_generation=0;

	CEvolutionaryAlgorithm* ea = new EvolutionaryAlgorithmImpl(this);
	generationalCriterion->setCounterEa(ea->getCurrentGenerationPtr());
	ea->addStoppingCriterion(generationalCriterion);
	ea->addStoppingCriterion(controlCStopingCriterion);
	ea->addStoppingCriterion(timeCriterion);	

	EZ_NB_GEN=((CGenerationalCriterion*)ea->stoppingCriteria[0])->getGenerationalLimit();
	EZ_current_generation=&(ea->currentGeneration);

	 return ea;
}

void EvolutionaryAlgorithmImpl::initializeParentPopulation(){
	for( unsigned int i=0 ; i< this->params->parentPopulationSize ; i++){
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		this->population->addIndividualParentPopulation(new IndividualImpl(),i);
	}
        this->population->actualParentPopulationSize = this->params->parentPopulationSize
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}


EvolutionaryAlgorithmImpl::EvolutionaryAlgorithmImpl(Parameters* params) : CEvolutionaryAlgorithm(params){
	this->population = (CPopulation*)new PopulationImpl(this->params->parentPopulationSize,this->params->offspringPopulationSize, this->params->pCrossover,this->params->pMutation,this->params->pMutationPerGene,this->params->randomGenerator,this->params);
	;
}

EvolutionaryAlgorithmImpl::~EvolutionaryAlgorithmImpl(){

}

PopulationImpl::PopulationImpl(size_t parentPopulationSize, size_t offspringPopulationSize, float pCrossover, float pMutation, float pMutationPerGene, CRandomGenerator* rg, Parameters* params) : CPopulation(parentPopulationSize, offspringPopulationSize, pCrossover, pMutation, pMutationPerGene, rg, params){
        ;
}

PopulationImpl::~PopulationImpl(){
}

\START_CUDA_GENOME_H_TPL

#ifndef PROBLEM_DEP_H
#define PROBLEM_DEP_H

//#include "CRandomGenerator.h"
#include <stdlib.h>
#include <iostream>
#include <CIndividual.h>
#include <Parameters.h>
class CRandomGenerator;
class CSelectionOperator;
class CGenerationalCriterion;
class CEvolutionaryAlgorithm;
class CPopulation;
class Parameters;

\INSERT_USER_CLASSES_DEFINITIONS

class IndividualImpl : public CIndividual {

public: // in EASEA the genome is public (for user functions,...)
	// Class members
  	\INSERT_GENOME

public:
	IndividualImpl();
	IndividualImpl(const IndividualImpl& indiv);
	virtual ~IndividualImpl();
	float evaluate();
	static size_t getCrossoverArrity(){ return 2; }
	float getFitness(){ return this->fitness; }
	CIndividual* crossover(CIndividual** p2);
	void optimiser(int currentIteration);
	void printOn(std::ostream& O) const;
	CIndividual* clone();

	size_t mutate(float pMutationPerGene);

	friend std::ostream& operator << (std::ostream& O, const IndividualImpl& B) ;
	void initRandomGenerator(CRandomGenerator* rg){ IndividualImpl::rg = rg;}

};


class ParametersImpl : public Parameters {
public:
	void setDefaultParameters(int argc, char** argv);
	CEvolutionaryAlgorithm* newEvolutionaryAlgorithm();
};

/**
 * @TODO ces functions devraient s'appeler weierstrassInit, weierstrassFinal etc... (en gros EASEAFinal dans le tpl).
 *
 */

void EASEAInit(int argc, char** argv);
void EASEAFinal(CPopulation* pop);
void EASEABeginningGenerationFunction(CEvolutionaryAlgorithm* evolutionaryAlgorithm);
void EASEAEndGenerationFunction(CEvolutionaryAlgorithm* evolutionaryAlgorithm);
void EASEAGenerationFunctionBeforeReplacement(CEvolutionaryAlgorithm* evolutionaryAlgorithm);


class EvolutionaryAlgorithmImpl: public CEvolutionaryAlgorithm {
public:
	EvolutionaryAlgorithmImpl(Parameters* params);
	virtual ~EvolutionaryAlgorithmImpl();
	void initializeParentPopulation();
};

class PopulationImpl: public CPopulation {
public:
        PopulationImpl(size_t parentPopulationSize, size_t offspringPopulationSize, float pCrossover, float pMutation, float pMutationPerGene, CRandomGenerator* rg, Parameters* params);
        virtual ~PopulationImpl();
	void optimisePopulation(CIndividual** population, size_t populationSize);
};

#endif /* PROBLEM_DEP_H */

\START_CUDA_MAKEFILE_TPL

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UNAME := $(shell uname)
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ifeq ($(UNAME),Darwin)
EASEALIB_PATH=$(EZ_PATH)libeasea/
else
EASEALIB_PATH=\EZ_PATHlibeasea/
endif

ifeq ($(UNAME),Darwin)
CXXFLAGS =      -O2 -g -Wall -fmessage-length=0 -I$(EASEALIB_PATH)include -I$(EZ_PATH)boost
else
CXXFLAGS =      -O2 -g -Wall -fmessage-length=0 -I$(EASEALIB_PATH)include
endif
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OBJS = EASEA.o EASEAIndividual.o 

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ifeq ($(UNAME),Darwin)
LIBS = $(EZ_PATH)boost/program_options.a
else
LIBS = -lboost_program_options
endif
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TARGET =	EASEA

$(TARGET):	$(OBJS)
	$(CXX) -o $(TARGET) $(OBJS) $(LIBS) -g $(EASEALIB_PATH)libeasea.a

	
#%.o:%.cpp
#	$(CXX) -c $(CXXFLAGS) $^

all:	$(TARGET)
clean:
	rm -f $(OBJS) $(TARGET)
easeaclean:
	rm -f $(TARGET) *.o *.cpp *.hpp EASEA.png EASEA.dat EASEA.prm EASEA.mak Makefile EASEA.vcproj EASEA.csv EASEA.r EASEA.plot
	
\START_VISUAL_TPL<?xml version="1.0" encoding="Windows-1252"?>
<VisualStudioProject
	ProjectType="Visual C++"
	Version="9,00"
	Name="EASEA"
	ProjectGUID="{E73D5A89-F262-4F0E-A876-3CF86175BC30}"
	RootNamespace="EASEA"
	Keyword="Win32Proj"
	TargetFrameworkVersion="196613"
	>
	<Platforms>
		<Platform
			Name="Win32"
		/>
	</Platforms>
	<ToolFiles>
	</ToolFiles>
	<Configurations>
		<Configuration
			Name="Release|Win32"
			OutputDirectory="$(SolutionDir)"
			IntermediateDirectory="$(ConfigurationName)"
			ConfigurationType="1"
			CharacterSet="1"
			WholeProgramOptimization="1"
			>
			<Tool
				Name="VCPreBuildEventTool"
			/>
			<Tool
				Name="VCCustomBuildTool"
			/>
			<Tool
				Name="VCXMLDataGeneratorTool"
			/>
			<Tool
				Name="VCWebServiceProxyGeneratorTool"
			/>
			<Tool
				Name="VCMIDLTool"
			/>
			<Tool
				Name="VCCLCompilerTool"
				Optimization="2"
				EnableIntrinsicFunctions="true"
				AdditionalIncludeDirectories="&quot;\EZ_PATHlibEasea&quot;"
				PreprocessorDefinitions="WIN32;NDEBUG;_CONSOLE"
				RuntimeLibrary="0"
				EnableFunctionLevelLinking="true"
				UsePrecompiledHeader="0"
				WarningLevel="3"
				DebugInformationFormat="3"
			/>
			<Tool
				Name="VCManagedResourceCompilerTool"
			/>
			<Tool
				Name="VCResourceCompilerTool"
			/>
			<Tool
				Name="VCPreLinkEventTool"
			/>
			<Tool
				Name="VCLinkerTool"
				LinkIncremental="1"
				AdditionalLibraryDirectories="&quot;\EZ_PATHlibEasea&quot;"
				GenerateDebugInformation="true"
				SubSystem="1"
				OptimizeReferences="2"
				EnableCOMDATFolding="2"
				TargetMachine="1"
			/>
			<Tool
				Name="VCALinkTool"
			/>
			<Tool
				Name="VCManifestTool"
			/>
			<Tool
				Name="VCXDCMakeTool"
			/>
			<Tool
				Name="VCBscMakeTool"
			/>
			<Tool
				Name="VCFxCopTool"
			/>
			<Tool
				Name="VCAppVerifierTool"
			/>
			<Tool
				Name="VCPostBuildEventTool"
			/>
		</Configuration>
	</Configurations>
	<References>
	</References>
	<Files>
		<Filter
			Name="Source Files"
			Filter="cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx"
			UniqueIdentifier="{4FC737F1-C7A5-4376-A066-2A32D752A2FF}"
			>
			<File
				RelativePath=".\EASEA.cpp"
				>
			</File>
			<File
				RelativePath=".\EASEAIndividual.cpp"
				>
			</File>
		</Filter>
		<Filter
			Name="Header Files"
			Filter="h;hpp;hxx;hm;inl;inc;xsd"
			UniqueIdentifier="{93995380-89BD-4b04-88EB-625FBE52EBFB}"
			>
			<File
				RelativePath=".\EASEAIndividual.hpp"
				>
			</File>
		</Filter>
		<Filter
			Name="Resource Files"
			Filter="rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav"
			UniqueIdentifier="{67DA6AB6-F800-4c08-8B7A-83BB121AAD01}"
			>
		</Filter>
	</Files>
	<Globals>
	</Globals>
</VisualStudioProject>

\START_EO_PARAM_TPL#****************************************
#                                         
#  EASEA.prm
#                                         
#  Parameter file generated by STD.tpl AESAE v1.0
#                                         
#***************************************
# --seed=0   # -S : Random number seed. It is possible to give a specific seed.

######    Evolution Engine    ######
--popSize=\POP_SIZE # -P : Population Size
--nbOffspring=\OFF_SIZE # -O : Nb of offspring (percentage or absolute)

######	  Stopping Criterions    #####
--nbGen=\NB_GEN #Nb of generations
--timeLimit=\TIME_LIMIT # Time Limit: desactivate with (0) (in Seconds)

######    Evolution Engine / Replacement    ######
--elite=\ELITE_SIZE  # Nb of elite parents (absolute)
--eliteType=\ELITISM # Strong (1) or weak (0) elitism (set elite to 0 for none)
--survivingParents=\SURV_PAR_SIZE # Nb of surviving parents (percentage or absolute)
--survivingOffspring=\SURV_OFF_SIZE  # Nb of surviving offspring (percentage or absolute)
--selectionOperator=\SELECTOR_OPERATOR # Selector: Deterministic, Tournament, Random, Roulette 
--selectionPressure=\SELECT_PRM
--reduceParentsOperator=\RED_PAR_OPERATOR 
--reduceParentsPressure=\RED_PAR_PRM
--reduceOffspringOperator=\RED_OFF_OPERATOR 
--reduceOffspringPressure=\RED_OFF_PRM
--reduceFinalOperator=\RED_FINAL_OPERATOR
--reduceFinalPressure=\RED_FINAL_PRM

######    Local Optimisation    ######
--optimiseIterations=\NB_OPT_IT
--baldwinism=\BALDWINISM # True (1) or False (0) baldwinism : keep optimised genome

#####	Stats Ouput 	#####
--printStats=\PRINT_STATS #print Stats to screen
--plotStats=\PLOT_STATS #plot Stats with gnuplot (requires Gnuplot)
--printInitialPopulation=0 #Print initial population
--printFinalPopulation=0 #Print final population
--generateCSVFile=\GENERATE_CSV_FILE
--generateGnuplotScript=\GENERATE_GNUPLOT_SCRIPT
--generateRScript=\GENERATE_R_SCRIPT
\TEMPLATE_END