void population::evolve(int gen) { //Wright-Fisher Model - Population size is held constant. Parental genotypes are chosen randomly.
for (int j=0; j<gen; j++){
vector<genome> newPop (N);
avgFit = 0;
for(int i=0; i<N; i++) {
if (r == 1) {
newPop[i] = progeny(selectParents());
avgFit += newPop[i].get_fit();
}
else if ((r != 0) && gsl_rng_uniform(rng) <= r) {
newPop[i] = progeny(selectParents());
avgFit += newPop[i].get_fit();
}
else {
if (neutral) {newPop[i] = pop[(int)gsl_rng_uniform_int(rng,N)];}
else {
double fit_rand = selective_weight[N-1]*gsl_rng_uniform(rng);
newPop[i] = pop[binarySearch_int(selective_weight, 0, N-1, fit_rand)];
avgFit += newPop[i].get_fit();
}
}
}
avgFit/= N;
pop = newPop;
gen_pop++;
update_weights();
//updateBlockSizes();
if (!neutral) {update_selection_weights();}
}
update_weights();
updateBlockSizes();
//if (WRITEHIST) {writeBlockHist();}
}
void Popu::createNewGen(){
///Apply Ellitism by keeping the 2 first chromosomes
newGen[0]=content[0];
newGen[1]=content[1];
///qDebug()<<"Ellistism performed"<<content[0].fitness<<content[1].fitness;
for (int i=2; i<popSize;i=i+2){
selectParents(&parentOne,&parentTwo,i);
///child=cross(parentOne,parentTwo); As crossover is a failure for now simply copy one parent as is
///child=parentOne;
///child=cross(parentOne,parentTwo);
///content[i]=child;
///child=cross(parentOne,parentTwo);
///content[i+1]=child;
//qDebug()<<"Generated"<<child.elements<<" route="<< child.routeLength;
//qDebug()<<"from parents"<< parentOne.elements<<parentTwo.elements;
///qDebug()<<"Generated"<< child.routeLength<<"from parents"<<parentOne.routeLength<<parentTwo.routeLength;
///if (child.routeLength >parentOne.routeLength) {child=parentOne;}
///if (child.routeLength >parentTwo.routeLength) {child=parentTwo;}
//qDebug()<<"Starting Mutation";
//qDebug()<<"Starting Mutation";
///mutate(i);
mutateImprove(i);
}
///qDebug()<<"New generation created";
content=newGen;
//newGen.clear();
sortContent();
}
pair<int,int> population::selectParents(){ //Parents are selected at random from population
pair<int,int> ancestors;
if (neutral) { //Neutral Evolution
ancestors.first = (int)gsl_rng_uniform_int(rng,N);
ancestors.second = (int)gsl_rng_uniform_int(rng,N);
}
else { //Selection!
double fit_rand1 = selective_weight[N-1]*gsl_rng_uniform(rng);
double fit_rand2 = selective_weight[N-1]*gsl_rng_uniform(rng);
ancestors.first = binarySearch_int(selective_weight, 0, N-1, fit_rand1);
ancestors.second = binarySearch_int(selective_weight, 0, N-1, fit_rand2);
}
//Recursive definition to ensure unique parents.
if (ancestors.second == ancestors.first) {
return selectParents();
}
else {
return ancestors;
}
}
/// Choisit n parents, cree un enfant, l'intensifie et met a jour la population
void runIteration(){
// Selection des parents
int nbParents=2;
selectParents(nbParents);
buildChildN(nbParents);
tColor=tChild;
initConflict();
bestFitnessValue=99999;
for (int i=0; i<nbLocalSearch && nbEdgesConflict > 0 ; i++) {
determineBestImprove();
if (nbEdgesConflict<bestFitnessValue) {
bestFitnessValue=nbEdgesConflict;
bestIter=i;
for (int j=0; j<nbSommets; j++) {
tBestSol[j]=tColor[j];
}
}
}
bestIterMean=((nbIterationsCross-1)*bestIterMean + bestIter)/nbIterationsCross;
printNewChild(nbParents);
saveFitnessValue();
// remplacement d'un parent si la solution n'est pas trouvee
if (nbEdgesConflict>0) {
updatePopulation();
}
}
