STD_MEM.tpl 17.4 KB
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\TEMPLATE_START
/**
 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;
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unsigned *EZ_NB_GEN;
unsigned *EZ_current_generation;
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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;


	return 0;
}

\START_CUDA_GENOME_CU_TPL

#include <fstream>
#include <time.h>
#include <string>
#include <sstream>
#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"
bool INSTEAD_EVAL_STEP = false;

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

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

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;
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  isImmigrant = false;
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}

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

IndividualImpl::~IndividualImpl(){
  \GENOME_DTOR
}


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

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void IndividualImpl::boundChecking(){
	\INSERT_BOUND_CHECKING
}

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string IndividualImpl::serialize(){
    ostringstream AESAE_Line(ios_base::app);
    \GENOME_SERIAL
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    AESAE_Line << this->fitness;
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    return AESAE_Line.str();
}

void IndividualImpl::deserialize(string Line){
    istringstream AESAE_Line(Line);
    string line;
    \GENOME_DESERIAL
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    AESAE_Line >> this->fitness;
    this->valid=true;
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    this->isImmigrant=false;
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}

IndividualImpl::IndividualImpl(const IndividualImpl& genome){

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


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

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;
}


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


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

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

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void PopulationImpl::optimisePopulation(CIndividual** population, unsigned populationSize){
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	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);

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

	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;

	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);

	this->printStats = setVariable("printStats",\PRINT_STATS);
	this->generateCSVFile = setVariable("generateCSVFile",\GENERATE_CSV_FILE);
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	this->generatePlotScript = setVariable("generatePlotScript",\GENERATE_GNUPLOT_SCRIPT);
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	this->generateRScript = setVariable("generateRScript",\GENERATE_R_SCRIPT);
	this->plotStats = setVariable("plotStats",\PLOT_STATS);
	this->printInitialPopulation = setVariable("printInitialPopulation",0);
	this->printFinalPopulation = setVariable("printFinalPopulation",0);
        this->savePopulation = setVariable("savePopulation",\SAVE_POPULATION);
        this->startFromFile = setVariable("startFromFile",\START_FROM_FILE);

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

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    this->remoteIslandModel = setVariable("remoteIslandModel",\REMOTE_ISLAND_MODEL);
    this->ipFile = (char*)setVariable("ipFile","\IP_FILE").c_str();
    this->migrationProbability = setVariable("migrationProbability",(float)\MIGRATION_PROBABILITY);
    this->serverPort = setVariable("serverPort",\SERVER_PORT);
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}

CEvolutionaryAlgorithm* ParametersImpl::newEvolutionaryAlgorithm(){

	pEZ_MUT_PROB = &pMutationPerGene;
	pEZ_XOVER_PROB = &pCrossover;
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	EZ_NB_GEN = (unsigned*)setVariable("nbGen",\NB_GEN);
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	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(){
        if(this->params->startFromFile){
          ifstream AESAE_File("EASEA.pop");
          string AESAE_Line;
          for( unsigned int i=0 ; i< this->params->parentPopulationSize ; i++){
                  getline(AESAE_File, AESAE_Line);
                  this->population->addIndividualParentPopulation(new IndividualImpl(),i);
                  ((IndividualImpl*)this->population->parents[i])->deserialize(AESAE_Line);
          }

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


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(){

}

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

PopulationImpl::~PopulationImpl(){
}

\START_CUDA_GENOME_H_TPL

#ifndef PROBLEM_DEP_H
#define PROBLEM_DEP_H

//#include "CRandomGenerator.h"
#include <stdlib.h>
#include <string>
#include <iostream>
#include <CIndividual.h>
#include <Parameters.h>

using namespace std;

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();
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	static unsigned getCrossoverArrity(){ return 2; }
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	float getFitness(){ return this->fitness; }
	CIndividual* crossover(CIndividual** p2);
	void optimiser(int currentIteration);
	void printOn(std::ostream& O) const;
	CIndividual* clone();

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	unsigned mutate(float pMutationPerGene);
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	void boundChecking();

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        string serialize();
        void deserialize(string AESAE_Line);

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

#endif /* PROBLEM_DEP_H */

\START_CUDA_MAKEFILE_TPL

UNAME := $(shell uname)

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ifeq($(shell uname -o 2>/dev/null),Msys)
	OS := MINGW
endif

ifneq ("$(OS)","")
	EZ_PATH=../../
endif

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EASEALIB_PATH=$(EZ_PATH)libeasea/

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CXXFLAGS =  -fopenmp -O2 -g -Wall -fmessage-length=0 -I$(EASEALIB_PATH)include -I$(EZ_PATH)boost
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OBJS = EASEA.o EASEAIndividual.o 

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LIBS = -lpthread -fopenmp
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ifneq ("$(OS)","")
	LIBS += -lw2_32 -lwinmm -L"C:\MinGW\lib"
endif

#USER MAKEFILE OPTIONS :
\INSERT_MAKEFILE_OPTION#END OF USER MAKEFILE OPTIONS
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TARGET =	EASEA

$(TARGET):	$(OBJS)
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	$(CXX) -o $(TARGET) $(OBJS) $(LDFLAGS) -g $(EASEALIB_PATH)libeasea.a $(EZ_PATH)boost/program_options.a $(LIBS)
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#%.o:%.cpp
#	$(CXX) -c $(CXXFLAGS) $^

all:	$(TARGET)
clean:
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ifneq ("^$(OS)","")
	-del $(OBJS) $(TARGET).exe
else
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	rm -f $(OBJS) $(TARGET)
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endif
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easeaclean:
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ifneq ("$(OS)","")
	-del $(TARGET).exe *.o *.cpp *.hpp EASEA.png EASEA.dat EASEA.prm EASEA.mak Makefile EASEA.vcproj EASEA.csv EASEA.r EASEA.plot EASEA.pop
else	
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	rm -f $(TARGET) *.o *.cpp *.hpp EASEA.png EASEA.dat EASEA.prm EASEA.mak Makefile EASEA.vcproj EASEA.csv EASEA.r EASEA.plot EASEA.pop
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endif
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\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
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--plotStats=\PLOT_STATS #plot Stats
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--printInitialPopulation=0 #Print initial population
--printFinalPopulation=0 #Print final population
--generateCSVFile=\GENERATE_CSV_FILE
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--generatePlotScript=\GENERATE_GNUPLOT_SCRIPT
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--generateRScript=\GENERATE_R_SCRIPT

#### Population save    ####
--savePopulation=\SAVE_POPULATION #save population to EASEA.pop file
--startFromFile=\START_FROM_FILE #start optimisation from EASEA.pop file

#### Remote Island Model ####
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--remoteIslandModel=\REMOTE_ISLAND_MODEL #To initialize communications with remote AESAE's
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--ipFile=\IP_FILE
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--migrationProbability=\MIGRATION_PROBABILITY #Probability to send an individual every generation
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--serverPort=\SERVER_PORT
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\TEMPLATE_END