// Return 0 if everything is OK, non-zero if error. If we did not set anything
// then we return non-zero (this is not an error, but we indicate that we did
// not do anything).
// The set method must set both the GA's parameter and the value in the
// parameter list (kind of stupid to maintain two copies of the same data, but
// oh well). The call to set on params is redundant for the times when this
// method is called *after* the parameter list has been updated, but it is
// necessary when this method is called directly by the user.
int
GAGeneticAlgorithm::setptr(const char* name, const void* value){
int status=1;
params.set(name, value); // redundant for some cases, but not others
if(strcmp(name, gaNnBestGenomes) == 0 ||
strcmp(name, gaSNnBestGenomes) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
nBestGenomes(*((int*)value));
status = 0;
}
else if(strcmp(name, gaNpopulationSize) == 0 ||
strcmp(name, gaSNpopulationSize) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
populationSize(*((int*)value));
status = 0;
}
else if(strcmp(name, gaNminimaxi) == 0 ||
strcmp(name, gaSNminimaxi) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
minimaxi(*((int*)value));
status = 0;
}
else if(strcmp(name, gaNnGenerations) == 0 ||
strcmp(name, gaSNnGenerations) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
nGenerations(*((int*)value));
status = 0;
}
else if(strcmp(name, gaNpConvergence) == 0 ||
strcmp(name, gaSNpConvergence) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((float*)value) << "'\n";
#endif
pConvergence(*((float*)value));
status = 0;
}
else if(strcmp(name, gaNnConvergence) == 0 ||
strcmp(name, gaSNnConvergence) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
nConvergence(*((int*)value));
status = 0;
}
else if(strcmp(name, gaNpCrossover) == 0 ||
strcmp(name, gaSNpCrossover) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((float*)value) << "'\n";
#endif
pCrossover(*((float*)value));
status = 0;
}
else if(strcmp(name, gaNpMutation) == 0 ||
strcmp(name, gaSNpMutation) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((float*)value) << "'\n";
#endif
pMutation(*((float*)value));
status = 0;
}
else if(strcmp(name,gaNscoreFrequency) == 0 ||
strcmp(name,gaSNscoreFrequency) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
stats.scoreFrequency(*((int*)value));
status = 0;
}
else if(strcmp(name,gaNflushFrequency) == 0 ||
strcmp(name,gaSNflushFrequency) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
stats.flushFrequency(*((int*)value));
status = 0;
}
else if(strcmp(name,gaNrecordDiversity) == 0 ||
strcmp(name,gaSNrecordDiversity) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
stats.recordDiversity(*((int*)value) ? gaTrue : gaFalse);
status = 0;
}
else if(strcmp(name,gaNselectScores) == 0 ||
strcmp(name,gaSNselectScores)==0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
stats.selectScores(*((int*)value));
status = 0;
}
else if(strcmp(name,gaNscoreFilename) == 0 ||
strcmp(name,gaSNscoreFilename) == 0){
#ifdef GA_DEBUG
cerr << "GAGeneticAlgorithm::setptr\n setting '" << name << "' to '" << ((char*)value) << "'\n";
#endif
char tmpname[64];
memcpy(tmpname, value, strlen((char*)value)+1);
stats.scoreFilename(tmpname);
status = 0;
}
return status;
}
// Evolve a new generation of genomes. When we start this routine, pop
// contains the current generation. When we finish, pop contains the new
// generation and oldPop contains the (no longer) current generation. The
// previous old generation is lost. We don't deallocate any memory, we just
// reset the contents of the genomes.
// The selection routine must return a pointer to a genome from the old
// population.
void
GASimpleGA::step()
{
int i, mut, c1, c2;
GAGenome *mom, *dad; // tmp holders for selected genomes
GAPopulation *tmppop; // Swap the old population with the new pop.
tmppop = oldPop; // When we finish the ++ we want the newly
oldPop = pop; // generated population to be current (for
pop = tmppop; // references to it from member functions).
// Generate the individuals in the temporary population from individuals in
// the main population.
for(i=0; i<pop->size()-1; i+=2){ // takes care of odd population
mom = &(oldPop->select());
dad = &(oldPop->select());
stats.numsel += 2; // keep track of number of selections
c1 = c2 = 0;
if(GAFlipCoin(pCrossover())){
stats.numcro += (*scross)(*mom, *dad,
&pop->individual(i), &pop->individual(i+1));
c1 = c2 = 1;
}
else{
pop->individual( i ).copy(*mom);
pop->individual(i+1).copy(*dad);
}
stats.nummut += (mut = pop->individual( i ).mutate(pMutation()));
if(mut > 0) c1 = 1;
stats.nummut += (mut = pop->individual(i+1).mutate(pMutation()));
if(mut > 0) c2 = 1;
stats.numeval += c1 + c2;
}
if(pop->size() % 2 != 0){ // do the remaining population member
mom = &(oldPop->select());
dad = &(oldPop->select());
stats.numsel += 2; // keep track of number of selections
c1 = 0;
if(GAFlipCoin(pCrossover())){
stats.numcro += (*scross)(*mom, *dad, &pop->individual(i), (GAGenome*)0);
c1 = 1;
}
else{
if(GARandomBit())
pop->individual( i ).copy(*mom);
else
pop->individual( i ).copy(*dad);
}
stats.nummut += (mut = pop->individual( i ).mutate(pMutation()));
if(mut > 0) c1 = 1;
stats.numeval += c1;
}
// Pass mpi_tasks and mpi_rank to the population clas
pop->mpi_tasks(vmpi_tasks);
pop->mpi_rank(vmpi_rank);
stats.numrep += pop->size();
pop->evaluate(gaTrue); // get info about current pop for next time
// If we are supposed to be elitist, carry the best individual from the old
// population into the current population. Be sure to check whether we are
// supposed to minimize or maximize.
if(minimaxi() == GAGeneticAlgorithm::MAXIMIZE) {
if(el && oldPop->best().score() > pop->best().score())
oldPop->replace(pop->replace(&(oldPop->best()), GAPopulation::WORST),
GAPopulation::BEST);
}
else {
if(el && oldPop->best().score() < pop->best().score())
oldPop->replace(pop->replace(&(oldPop->best()), GAPopulation::WORST),
GAPopulation::BEST);
}
stats.update(*pop); // update the statistics by one generation
}
