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Commit b301ea50 authored by maitre's avatar maitre
Browse files

Minimaxi 

define std_tpl
out/input file disabled
parent f50f76db
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EaseaLex.l
View file @ b301ea50
......@@ -543,10 +543,16 @@ exponent ([Ee][+-]?[0-9]+)
<TEMPLATE_ANALYSIS>"\\STEADYSTATE" {if ((fREPL_PERC==0)||(!strcmp(sREPLACEMENT,"Incremental"))||(!strcmp(sREPLACEMENT,"Simple")))
fprintf(fpOutputFile,"// undefined ");}
<TEMPLATE_ANALYSIS>"\\COMMENT" {if (mystricmp(sREPLACEMENT,"SSGA")) fprintf(fpOutputFile,"//");}
<TEMPLATE_ANALYSIS>"\\MINIMAXI" {switch (TARGET) { case GALIB : fprintf(fpOutputFile,"%d",nMINIMISE? -1:1); break;
<TEMPLATE_ANALYSIS>"\\MINIMAXI" {
switch (TARGET) {
case GALIB : fprintf(fpOutputFile,"%d",nMINIMISE? -1:1); break;
case EO : fprintf(fpOutputFile,"%s",nMINIMISE? "eoMinimizingFitness" : "eoMaximizingFitness"); break;
case DREAM : fprintf(fpOutputFile,"%s",nMINIMISE? "false" : "true"); break;
} }
case STD:
case CUDA: fprintf(fpOutputFile,"%s",(nMINIMISE? "false" : "true")); break;
}
}
<TEMPLATE_ANALYSIS>"\\ELITIST_REPLACE_NAME" {if (TARGET==EO) {if (bELITISM) fprintf(fpOutputFile,"standardR");
else fprintf(fpOutputFile,"r");}}
<TEMPLATE_ANALYSIS>"\\ELITISM" {
......
exemples/cuda_listsort/listsort.ez
View file @ b301ea50
......@@ -44,7 +44,7 @@ void cross(GenomeClass *pChild, GenomeClass *pParent, int locus){
\Finalization function:
std::cout << "fonction de finalization svp" << std::endl;
std::cout << population ;
// std::cout << population ;
\end
\GenomeClass::initialiser:
......
tpl/STD.tpl
View file @ b301ea50
......@@ -52,6 +52,8 @@ int main(int argc, char** argv){
delete pop;
delete sc;
return 0;
}
......@@ -62,6 +64,8 @@ int main(int argc, char** argv){
#include <string.h>
#include <fstream>
#define STD_TPL
extern RandomGenerator* globalRandomGenerator;
\INSERT_USER_DECLARATIONS
......@@ -245,17 +249,17 @@ void EvolutionaryAlgorithm::addStoppingCriterion(StoppingCriterion* sc){
void EvolutionaryAlgorithm::runEvolutionaryLoop(){
std::vector<Individual*> tmpVect;
if( inputfile ){
DEBUG_PRT("Loading initial population from file : %s",inputfile->c_str());
std::ifstream ifs("essai.out");
DEBUG_PRT("parent population size in ea %d",population->parentPopulationSize);
//population->parents = new Individual*[population->parentPopulationSize];
boost::archive::text_iarchive ia(ifs);
/* if( inputfile ){ */
/* DEBUG_PRT("Loading initial population from file : %s",inputfile->c_str()); */
/* std::ifstream ifs("essai.out"); */
/* DEBUG_PRT("parent population size in ea %d",population->parentPopulationSize); */
/* //population->parents = new Individual*[population->parentPopulationSize]; */
/* boost::archive::text_iarchive ia(ifs); */
ia >> *population;
population->syncInVector();
//ia >> *population;
}
/* //ia >> *population; */
/* population->syncInVector(); */
/* //ia >> *population; */
/* } */
std::cout << "Parent's population initializing "<< std::endl;
this->population->initializeParentPopulation();
......@@ -275,7 +279,7 @@ void EvolutionaryAlgorithm::runEvolutionaryLoop(){
population->reduceTotalPopulation();
currentGeneration += 1;
currentAverageFitness=0.0;
currentSTDEV=0.0;
......@@ -283,48 +287,54 @@ void EvolutionaryAlgorithm::runEvolutionaryLoop(){
showPopulationStats();
\INSERT_GEN_FCT_CALL
currentGeneration += 1;
}
population->sortParentPopulation();
//std::cout << *population << std::endl;
std::cout << "Generation : " << currentGeneration << std::endl;
if( outputfile ){
DEBUG_PRT("Dumping final population to file : %s",outputfile->c_str());
std::ofstream ofs(outputfile->c_str());
boost::archive::text_oarchive oa(ofs);
population->syncOutVector();
oa << *population ;
/* if( outputfile ){ */
/* DEBUG_PRT("Dumping final population to file : %s",outputfile->c_str()); */
/* std::ofstream ofs(outputfile->c_str()); */
/* boost::archive::text_oarchive oa(ofs); */
/* population->syncOutVector(); */
/* oa << *population ; */
}
/* } */
}
void EvolutionaryAlgorithm::showPopulationStats(){
//Calcul de la moyenne et de l'ecart type
for(int i=0; i<population->parentPopulationSize; i++){
currentAverageFitness+=population->parents[i]->getFitness();
if(currentGeneration== 1 && i==0)
population->Best=population->parents[i];
else{
if(population->parents[i]->getFitness()<population->Best->getFitness())
population->Best=population->parents[i];
}
}
currentAverageFitness/=population->parentPopulationSize;
for(int i=0; i<population->parentPopulationSize; i++){
currentSTDEV+=(population->parents[i]->getFitness()-currentAverageFitness)*(population->parents[i]->getFitness()-currentAverageFitness);
}
currentSTDEV/=population->parentPopulationSize;
currentSTDEV=sqrt(currentSTDEV);
//Affichage
if(currentGeneration==1){
printf("GEN\tTIME\tEVAL\tBEST\t\tAVG\t\tSTDEV\n\n");
printf("%d\t%d\t%d\t%f\t%f\t%f\n",currentGeneration,0,population->currentEvaluationNb,population->Best->getFitness(),currentAverageFitness,currentSTDEV);
}
else
printf("%d\t%d\t%d\t%f\t%f\t%f\n",currentGeneration,0,population->currentEvaluationNb,population->Best->getFitness(),currentAverageFitness,currentSTDEV);
//Calcul de la moyenne et de l'ecart type
if( currentGeneration == 0 )
population->Best=population->parents[0];
for(int i=0; i<population->parentPopulationSize; i++){
currentAverageFitness+=population->parents[i]->getFitness();
#if \MINIMAXI
if(population->parents[i]->getFitness()>population->Best->getFitness())
#else
if(population->parents[i]->getFitness()<population->Best->getFitness())
#endif
population->Best=population->parents[i];
}
currentAverageFitness/=population->parentPopulationSize;
for(int i=0; i<population->parentPopulationSize; i++){
currentSTDEV+=(population->parents[i]->getFitness()-currentAverageFitness)*(population->parents[i]->getFitness()-currentAverageFitness);
}
currentSTDEV/=population->parentPopulationSize;
currentSTDEV=sqrt(currentSTDEV);
//Affichage
if(currentGeneration==1){
printf("GEN\tTIME\tEVAL\tBEST\t\tAVG\t\tSTDEV\n\n");
printf("%d\t%d\t%d\t%f\t%f\t%f\n",currentGeneration,0,population->currentEvaluationNb,population->Best->getFitness(),currentAverageFitness,currentSTDEV);
}
else
printf("%d\t%d\t%d\t%f\t%f\t%f\n",currentGeneration,0,population->currentEvaluationNb,population->Best->getFitness(),currentAverageFitness,currentSTDEV);
}
bool EvolutionaryAlgorithm::allCriteria(){
......
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