Memetic Version for STD

examples/memetic_std/memetic_weierstrass.ez

+/*_________________________________________________________
+
+Test functions
+log normal adaptive mutation
+Selection operator: Tournament
+__________________________________________________________*/
+
+\User declarations :
+#define SIZE 100
+#define X_MIN -1.
+#define X_MAX 1.
+#define ITER 120      
+#define Abs(x) ((x) < 0 ? -(x) : (x))
+#define MAX(x,y) ((x)>(y)?(x):(y))
+#define MIN(x,y) ((x)<(y)?(x):(y))
+#define SIGMA  1.                     /*  mutation parameter */
+#define PI 3.141592654
+
+ 
+float pMutPerGene=0.1;
+
+
+\end
+
+\User functions:
+//fitness function
+#include <math.h>
+
+__device__ __host__ inline float Weierstrass(float x[SIZE], int n)  // Weierstrass multimidmensionnel h = 0.25
+{
+   float res = 0.;
+   float val[SIZE];
+   float b=2.;
+   float h = 0.25;
+
+   for (int i = 0;i<n; i++) {
+	val[i] = 0.;
+    	for (int k=0;k<ITER;k++)
+		val[i] += pow(b,-(float)k*h) * sin(pow(b,(float)k)*x[i]);
+	res += Abs(val[i]);
+	}
+   return (res);
+} 
+
+float gauss()
+/* Generates a normally distributed random value with variance 1 and 0 mean.
+    Algorithm based on "gasdev" from Numerical recipes' pg. 203. */
+{
+  int iset = 0;
+  float gset = 0.0;
+  float v1 = 0.0, v2 = 0.0, r = 0.0;
+  float factor = 0.0;
+
+  if (iset) {
+        iset = 0;
+        return gset;
+      	}
+  else {    
+        do {
+            v1 = (float)random(0.,1.) * 2.0 - 1.0;
+            v2 = (float)random(0.,1.) * 2.0 - 1.0;
+            r = v1 * v1 + v2 * v2;
+	                }
+        while (r > 1.0);
+        factor = sqrt (-2.0 * log (r) / r);
+        gset = v1 * factor;
+        iset = 1;
+        return (v2 * factor);
+    	}
+}
+\end
+
+
+
+\Before everything else function:
+  //cout<<"Before everything else function called "<<endl;
+\end
+
+\After everything else function:
+  //cout << "After everything else function called" << endl;
+\end
+
+\At the beginning of each generation function:
+  //cout << "At the beginning of each generation function called" << endl;
+\end
+
+\At the end of each generation function:
+  //cout << "At the end of each generation function called" << endl;
+\end
+
+\At each generation before reduce function:
+  //cout << "At each generation before replacement function called" << endl;
+\end
+
+\User classes :
+
+GenomeClass { 
+  float x[SIZE];
+  float sigma[SIZE]; // auto-adaptative mutation parameter
+}
+\end
+
+\GenomeClass::display:
+/* 	 for( size_t i=0 ; i<SIZE ; i++){ */
+/* 	      //     cout << Genome.x[i] << ":" << Genome.sigma[i] << "|"; */
+/* 	      printf("%.02f:%.02f|",Genome.x[i],Genome.sigma[i]); */
+/* 	 }	       */
+\end
+
+\GenomeClass::initialiser : // "initializer" is also accepted
+  for(int i=0; i<SIZE; i++ ) {
+     	Genome.x[i] = (float)random(X_MIN,X_MAX);
+	Genome.sigma[i]=(float)random(0.,0.5);
+	}
+\end
+
+\GenomeClass::crossover :
+  for (int i=0; i<SIZE; i++)
+  {
+    float alpha = (float)random(0.,1.); // barycentric crossover
+     child.x[i] = alpha*parent1.x[i] + (1.-alpha)*parent2.x[i];
+  }
+\end
+
+\GenomeClass::mutator : // Must return the number of mutations
+  int NbMut=0;
+  float pond = 1./sqrt((float)SIZE);
+
+    for (int i=0; i<SIZE; i++)
+    if (tossCoin(pMutPerGene)){
+    	NbMut++;
+       	Genome.sigma[i] = Genome.sigma[i] * exp(SIGMA*pond*(float)gauss());
+       	Genome.sigma[i] = MIN(0.5,Genome.sigma[0]);              
+       	Genome.sigma[i] = MAX(0.,Genome.sigma[0]);
+       	Genome.x[i] += Genome.sigma[i]*(float)gauss();
+       	Genome.x[i] = MIN(X_MAX,Genome.x[i]);              // pour eviter les depassements
+       	Genome.x[i] = MAX(X_MIN,Genome.x[i]);
+    	}
+return NbMut;
+\end
+
+\GenomeClass::evaluator : // Returns the score
+  float Score= 0.0;
+  Score= Weierstrass(Genome.x, SIZE);         
+  return Score;
+\end
+
+\GenomeClass::optimiser : // Optimises the Genome
+  for(int i=0; i<SIZE; i++){
+	if(random(0.,1.)<0.2)
+		Genome.x[i]+=0.1;	
+  }
+\end
+
+\User Makefile options: 
+CPPFLAGS+=
+\end
+
+\Default run parameters :        // Please let the parameters appear in this order
+  Number of generations : 100     	// NB_GEN
+  Time limit: 0 			// In seconds, 0 to deactivate
+  Population size : 10			//POP_SIZE
+  Offspring size : 10 // 40% 
+  Mutation probability : 1       // MUT_PROB
+  Crossover probability : 1      // XOVER_PROB
+  Evaluator goal : minimise      // Maximise
+  Selection operator: Tournament 2.0
+  Surviving parents: 100%//percentage or absolute  
+  Surviving offspring: 100%
+  Reduce parents operator: Tournament 2
+  Reduce offspring operator: Tournament 2
+  Final reduce operator: Tournament 2
+
+  Elitism: Strong			//Weak or Strong
+  Elite: 1
+
+  Number of optimisation iterations : 100 //Number of optimisation iteration
+  Baldwinism : true //True or False (Lamarckism : keep optimised Genome
+
+  Print stats:1				//Default: 1
+  Generate csv stats file:0			
+  Generate gnuplot script:0
+  Generate R script:0
+  Plot stats:0				//Default: 0
+\end
+	

tpl/STD_MEM.tpl

+\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"
+
+
+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
+
+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++){
+		this->population->addIndividualParentPopulation(new IndividualImpl());
+	}
+}
+
+
+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
+
+EASEALIB_PATH=\EZ_PATHlibeasea/#/home/kruger/Bureau/Easea/libeasea/
+
+CXXFLAGS =	-O2 -g -Wall -fmessage-length=0 -I$(EASEALIB_PATH)include
+
+OBJS = EASEA.o EASEAIndividual.o 
+
+LIBS = -lboost_program_options 
+
+TARGET =	EASEA
+
+$(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"?>