// #include "BitEvolver/Includes.h" #include "BitEvolver/Random.h" #include "BitEvolver/Population.h" #include "BitEvolver/Breeder.h" #include "BitEvolver/Chromosome.h" // #include #include #include #include #include #include // namespace BitEvolver { // using std::cout; using std::endl; // Population::Population() { // this->InitRandomGenerator(); this->InitBreeder(); // this->Reset(); } // void Population::Reset() { // this->population_size = BIT_EVOLVER_POPULATION_DEFAULT_POPULATION_SIZE; this->SetMutationRate(BIT_EVOLVER_POPULATION_DEFAULT_MUTATE_RATE); this->evolution_number = 0; // this->RandomizePopulation(this->population_size); } // void Population::ClearPopulation() { // this->chromosomes.clear(); this->evolution_number = 0; } // void Population::InitRandomPopulation(int _population_size, int _bit_length) { // this->population_size = _population_size; // this->RandomizePopulation(_bit_length); } // void Population::RandomizePopulation(int _bit_length) { // std::shared_ptr chromosome; int i; // this->ClearPopulation(); for ( i=0; ipopulation_size; i++ ) { // chromosome = std::shared_ptr( new Chromosome( this->random, _bit_length ) ); this->chromosomes.push_back(chromosome); } } // void Population::PopulationChanged() { // this->population_needs_sorting = true; } // std::vector> Population::GetChromosomes() { return this->chromosomes; } // void Population::GetChromosomes(std::shared_ptr>> _chromosomes) { // _chromosomes->clear(); for ( std::shared_ptr chromosome : this->chromosomes) { _chromosomes->push_back(chromosome); } } // std::shared_ptr Population::GetChampion() { // this->EnsureSortedPopulation(); // if ( this->chromosomes.size() > 0 ) { return this->chromosomes[0]; } return nullptr; } // double Population::GetAverageFitness() { return this->GetAverageFitness(this->chromosomes); } // double Population::GetAverageFitness(std::vector> _chromosomes) { // double fitness_sum; double fitness_average; // fitness_sum = 0; for ( std::shared_ptr chromosome : _chromosomes ) { fitness_sum += chromosome->GetFitness(); } // fitness_average = 0; if ( _chromosomes.size() > 0 ) { fitness_average = fitness_sum / _chromosomes.size(); } return fitness_average; } // void Population::SetCrossoverPoint(double p) { // this->crossover_point = p; } // double Population::GetCrossoverPoint() { // return this->crossover_point; } // void Population::SetCrossoverType(Enums::CrossoverType t) { // this->crossover_type = t; } // Enums::CrossoverType Population::GetCrossoverType() { // return this->crossover_type; } // void Population::SetMutationRate(double r) { // this->mutation_rate = r; } // double Population::GetMutationRate() { // return this->mutation_rate; } // void Population::Evolve() { // std::shared_ptr > > population_new; // if ( this->chromosomes.size() == 0 ) { return; } // this->EnsureSortedPopulation(); // population_new = std::shared_ptr< std::vector< std::shared_ptr > >( new std::vector>() ); // Start the new population off with our champion, // so the best score always carries over (elitism = 1 unit) #warning "Elitism is only 1 right now" population_new->push_back(this->chromosomes[0]); // Breed the new population this->BreedNewPopulation(population_new, (int)this->chromosomes.size()); // Replace old population with the new this->chromosomes = *population_new; this->evolution_number++; // this->PopulationChanged(); } // int Population::GetEvolutionNumber() { return this->evolution_number; } // void Population::PrintPopulation() { // this->EnsureSortedPopulation(); this->PrintPopulation(this->chromosomes); } // void Population::PrintPopulation(std::vector> _chromosomes) { // for ( std::shared_ptr chromosome : chromosomes ) { cout << chromosome->ToString() << endl; } cout << "Average Fitness --> " << this->GetAverageFitness(_chromosomes) << endl; } // void Population::InitRandomGenerator() { // this->random = std::shared_ptr( new Random() ); } // void Population::InitBreeder() { // if ( !this->random ) { throw std::runtime_error("Population::InitBreeder() - Should come after InitRandomGenerator()"); } // this->breeder = std::shared_ptr( new Breeder( this->random ) ); } // void Population::EnsureSortedPopulation() { // if ( !this->population_needs_sorting ) { return; } // Yay std::sort std::sort( this->chromosomes.begin(), this->chromosomes.end(), []( std::shared_ptr& left, std::shared_ptr& right ) -> bool { // if ( left->GetFitness() > right->GetFitness() ) { return true; } return false; } ); // this->population_needs_sorting = false; } // void Population::BreedNewPopulation(std::shared_ptr>> population_new, int size) { // std::vector> threads; std::shared_ptr thread; int thread_count, i ; // thread_count = this->GetThreadCountSuggestion(); // for ( i=0; i( new std::thread(&Population::BreedNewPopulation_Thread, this, population_new, size) ); threads.push_back(thread); } // for ( i=0; i<(int)threads.size(); i++) { threads[i]->join(); } } // void Population::BreedNewPopulation_Thread(std::shared_ptr>> population_new, int size) { // std::shared_ptr kiddo; // while ( (int)population_new->size() < size ) { // kiddo = this->BreedChild(); // Mutexed this->breed_mutex.lock(); if ( (int)population_new->size() < size ) { population_new->push_back(kiddo); } this->breed_mutex.unlock(); } } // std::shared_ptr Population::BreedChild() { // std::shared_ptr mama, papa, kiddo ; // Pick two parents mama = this->PickChromosomeForBreeding(); papa = this->PickChromosomeForBreeding(); // kiddo = this->breeder->Breed( mama, papa, this->crossover_type, this->crossover_point, this->mutation_rate ); return kiddo; } /** (1) We want to pick the best chromosomes to repopulate (2) [not in lecture] We want to add a bit of randomness to the selection process, such that the worst chromosomes can still possibly be picked, and the best can still possibly be not-picked; Their fitness only increases the probability they're picked. */ std::shared_ptr Population::PickChromosomeForBreeding() { // double normal; int chromosome_index; // this->EnsureSortedPopulation(); /** Grab normal with 0 at the mean, and the standard deviation equal to 1/2 of the population size. Then make that an absolute value. This will make top/best chromosomes more likely to be picked, with the far/low end being much less likely */ #warning "Need to upgrade this to Roulette Wheel" // Repeat as needed, since the normal generator might actually // give us an out-of-bounds result sometimes while ( true ) { // normal = this->random->GetNormal(0, 0.5); chromosome_index = abs( normal * this->chromosomes.size() ); if ( chromosome_index >= 0 && chromosome_index < (int)this->chromosomes.size() ) { break; } } // return this->chromosomes[chromosome_index]; } // int Population::GetThreadCountSuggestion() { // int thread_count; // thread_count = std::thread::hardware_concurrency(); return thread_count; } };