void crossover (int j)
{
struct node *p1_node = NULL;
struct node *p2_node = NULL;
struct node *t1_node = NULL;
int point1 = 0;
int point2 = 0;
int p1 = tournament_selection();
int p2 = p1;
int i = 0;
if(number_of_nondominated > 1) while (p1 == p2) p2 = tournament_selection();
point1 = (rand() % population[p1].size) + 1;
point2 = (rand() % population[p2].size) + 1;
population[j].root = copy_tree(population[p1].root);
p1_node = find_node(point1,&i,population[j].root);
i = 0;
p2_node = find_node(point2,&i,population[p2].root);
t1_node = copy_tree(p2_node);
destroy_tree(p1_node -> left);
destroy_tree(p1_node -> right);
p1_node -> key_type = t1_node -> key_type;
p1_node -> key_value = t1_node -> key_value;
p1_node -> left = t1_node -> left;
p1_node -> right = t1_node -> right;
free(t1_node);
}
void mutate (int j)
{
int p = tournament_selection();
population[j].root = copy_tree(population[p].root);
int ind = (rand() % get_tree_size(population[j].root)) + 1;
struct node *n_node = NULL;
struct node *e_node = NULL;
if ( ind == 1 )
{
destroy_tree(population[j].root);
population[j].root = create_random_node();
population[j].age = 0;
}
else
{
int i = 0;
n_node = find_node(ind,&i,population[j].root);
e_node = create_random_node();
destroy_tree(n_node -> left);
destroy_tree(n_node -> right);
n_node -> key_type = e_node -> key_type;
n_node -> key_value = e_node -> key_value;
n_node -> left = e_node -> left;
n_node -> right = e_node -> right;
free(e_node);
}
}
void Generation::produce_offspring(genetic_parameters const& genetic_param, std::vector<bound> const& param_bounds)
{
// Initialize random generator
std::random_device rd;
std::array<unsigned int, std::mt19937::state_size> seed_data;
std::generate_n(seed_data.begin(), seed_data.size(), std::ref(rd));
std::seed_seq seq(std::begin(seed_data), std::end(seed_data));
std::mt19937 engine(seq);
// Check if the number of chromosomes is even
size_t pairs(0);
if (size % 2 == 0) {
pairs = size / 2;
}
else {
pairs = (size + 1) / 2;
}
// Select the pairs of parents and produce an offspring
for (size_t i = 0; i < pairs; ++i) {
// Select parents via tournament selection
Chromosome parent1 = chromosomes[tournament_selection(engine)];
Chromosome parent2 = chromosomes[tournament_selection(engine)];
// Crossover parents
crossover_chromosomes(parent1, parent2, genetic_param.prob_crossover, engine);
// Mutate parents
mutate_chromosome(parent1, genetic_param.prob_mutation, param_bounds, engine);
mutate_chromosome(parent2, genetic_param.prob_mutation, param_bounds, engine);
// Save new chromosomes
offspring.push_back(parent1);
if (2 * (i + 1) <= size) {
offspring.push_back(parent2);
}
}
// Save new generation
chromosomes = offspring;
offspring.clear();
}
void evolve_one_subpopulation(int *current_subpop_index, t_chromosome ** sub_populations, int generation_index, t_parameters *params, t_graph *training_graphs, int num_training_graphs, int num_variables, double* vars_values)
#endif
{
int pop_index = 0;
while (*current_subpop_index < params->num_sub_populations) {// still more subpopulations to evolve?
#ifdef USE_THREADS
while (!mutex->try_lock()) {}// create a lock so that multiple threads will not evolve the same sub population
pop_index = *current_subpop_index;
(*current_subpop_index)++;
mutex->unlock();
#else
pop_index = *current_subpop_index;
(*current_subpop_index)++;
#endif
// pop_index is the index of the subpopulation evolved by the current thread
if (pop_index < params->num_sub_populations) {
t_chromosome *a_sub_population = sub_populations[pop_index];
t_chromosome offspring1, offspring2;
allocate_chromosome(offspring1, *params);
allocate_chromosome(offspring2, *params);
double *partial_values_array = new double[params->code_length];
if (generation_index == 0) {
for (int i = 0; i < params->sub_population_size; i++) {
generate_random_chromosome(a_sub_population[i], *params, num_variables);
a_sub_population[i].fitness = compute_fitness(a_sub_population[i], training_graphs, num_training_graphs, num_variables, vars_values, partial_values_array);
}
// sort ascendingly by fitness inside this population
qsort((void *)a_sub_population, params->sub_population_size, sizeof(a_sub_population[0]), sort_function);
}
else // next generations
for (int k = 0; k < params->sub_population_size; k += 2) {
// we increase by 2 because at each step we create 2 offspring
// choose the parents using binary tournament
int r1 = tournament_selection(a_sub_population, params->sub_population_size, 2);
int r2 = tournament_selection(a_sub_population, params->sub_population_size, 2);
// crossover
double p_0_1 = rand() / double(RAND_MAX); // a random number between 0 and 1
if (p_0_1 < params->crossover_probability)
one_cut_point_crossover(a_sub_population[r1], a_sub_population[r2], *params, offspring1, offspring2);
else {// no crossover so the offspring are a copy of the parents
copy_individual(offspring1, a_sub_population[r1], *params);
copy_individual(offspring2, a_sub_population[r2], *params);
}
// mutate the result and compute fitness
mutation(offspring1, *params, num_variables);
offspring1.fitness = compute_fitness(offspring1, training_graphs, num_training_graphs, num_variables, vars_values, partial_values_array);
// mutate the other offspring too
mutation(offspring2, *params, num_variables);
offspring2.fitness = compute_fitness(offspring2, training_graphs, num_training_graphs, num_variables, vars_values, partial_values_array);
// replace the worst in the population
if (offspring1.fitness < a_sub_population[params->sub_population_size - 1].fitness) {
copy_individual(a_sub_population[params->sub_population_size - 1], offspring1, *params);
qsort((void *)a_sub_population, params->sub_population_size, sizeof(a_sub_population[0]), sort_function);
}
if (offspring2.fitness < a_sub_population[params->sub_population_size - 1].fitness) {
copy_individual(a_sub_population[params->sub_population_size - 1], offspring2, *params);
qsort((void *)a_sub_population, params->sub_population_size, sizeof(a_sub_population[0]), sort_function);
}
}
delete_chromosome(offspring1);
delete_chromosome(offspring2);
delete[] partial_values_array;
}
}
}
//---------------------------------------------------------------------------
void start_steady_state_mep(parameters ¶ms, double **training_data, double* target, int num_training_data, int num_variables) // Steady-State
{
// a steady state approach:
// we work with 1 population
// newly created individuals will replace the worst existing ones (only if they are better).
// allocate memory
chromosome *population;
population = new chromosome[params.pop_size];
for (int i = 0; i < params.pop_size; i++)
allocate_chromosome(population[i], params);
chromosome offspring1, offspring2;
allocate_chromosome(offspring1, params);
allocate_chromosome(offspring2, params);
double ** eval_matrix;
allocate_partial_expression_values(eval_matrix, num_training_data, params.code_length);
// initialize
for (int i = 0; i < params.pop_size; i++) {
generate_random_chromosome(population[i], params, num_variables);
if (params.problem_type == PROBLEM_TYPE_REGRESSION)
fitness_regression(population[i], params.code_length, num_variables, num_training_data, training_data, target, eval_matrix);
else
fitness_classification(population[i], params.code_length, num_variables, params.num_classes, num_training_data, training_data, target, eval_matrix);
}
// sort ascendingly by fitness
qsort((void *)population, params.pop_size, sizeof(population[0]), sort_function);
printf("generation %d, best fitness = %lf\n", 0, population[0].fitness);
for (int g = 1; g < params.num_generations; g++) {// for each generation
for (int k = 0; k < params.pop_size; k += 2) {
// choose the parents using binary tournament
int r1 = tournament_selection(population, params.pop_size, 2);
int r2 = tournament_selection(population, params.pop_size, 2);
// crossover
double p = rand() / double(RAND_MAX);
if (p < params.crossover_probability)
one_cut_point_crossover(population[r1], population[r2], params, offspring1, offspring2);
else {// no crossover so the offspring are a copy of the parents
copy_individual(offspring1, population[r1], params);
copy_individual(offspring2, population[r2], params);
}
// mutate the result and compute fitness
mutation(offspring1, params, num_variables);
if (params.problem_type == PROBLEM_TYPE_REGRESSION)
fitness_regression(offspring1, params.code_length, num_variables, num_training_data, training_data, target, eval_matrix);
else
fitness_classification(offspring1, params.code_length, num_variables, params.num_classes, num_training_data, training_data, target, eval_matrix);
// mutate the other offspring and compute fitness
mutation(offspring2, params, num_variables);
if (params.problem_type == PROBLEM_TYPE_REGRESSION)
fitness_regression(offspring2, params.code_length, num_variables, num_training_data, training_data, target, eval_matrix);
else
fitness_classification(offspring2, params.code_length, num_variables, params.num_classes, num_training_data, training_data, target, eval_matrix);
// replace the worst in the population
if (offspring1.fitness < population[params.pop_size - 1].fitness) {
copy_individual(population[params.pop_size - 1], offspring1, params);
qsort((void *)population, params.pop_size, sizeof(population[0]), sort_function);
}
if (offspring2.fitness < population[params.pop_size - 1].fitness) {
copy_individual(population[params.pop_size - 1], offspring2, params);
qsort((void *)population, params.pop_size, sizeof(population[0]), sort_function);
}
}
printf("generation %d, best fitness = %lf\n", g, population[0].fitness);
}
// print best chromosome
print_chromosome(population[0], params, num_variables);
// free memory
delete_chromosome(offspring1);
delete_chromosome(offspring2);
for (int i = 0; i < params.pop_size; i++)
delete_chromosome(population[i]);
delete[] population;
delete_data(training_data, target, num_training_data);
delete_partial_expression_values(eval_matrix, params.code_length);
}
int main()
{
// Initial population
std::vector<grid> population;
int largest = 0;
for(int i = 0; i < 100; ++i)
{
grid g;
while(g.can_move())
{
g.action(rand_action());
}
population.emplace_back(g);
}
for(unsigned int i = 0; i < 100000; ++i)
{
unsigned int parent_a = tournament_selection(population);
unsigned int parent_b = tournament_selection(population);
grid child_a;
grid child_b;
one_point_crossover(population.at(parent_a).actions(),
population.at(parent_b).actions(),
child_a,
child_b);
population.emplace_back(child_a);
population.emplace_back(child_b);
sort_population(population);
population.pop_back();
population.pop_back();
if(largest < population.at(0).score())
{
largest = population.at(0).score();
std::cout << largest << std::endl;
}
sort_population_random(population);
}
sort_population(population);
population.at(0).print();
for(auto const& p : population)
{
std::cout << p.score() << std::endl;
}
return 0;
}
//--------------------------------------------------------------------
void start_steady_state_ga(int pop_size, int num_gens, int num_dims, int num_bits_per_dimension, double pcross, double pm, double min_x, double max_x)
// Steady-State genetic algorithm
// each step:
// pick 2 parents, mate them,
// mutate the offspring
// and replace the worst in the population
// (only if offspring are better)
{
// allocate memory
b_chromosome *population;
population = (b_chromosome*)malloc(pop_size * sizeof(b_chromosome));
for (int i = 0; i < pop_size; i++)
population[i].x = (char*)malloc(num_dims * num_bits_per_dimension);
b_chromosome offspring1, offspring2;
offspring1.x = (char*)malloc(num_dims * num_bits_per_dimension);
offspring2.x = (char*)malloc(num_dims * num_bits_per_dimension);
// initialize
for (int i = 0; i < pop_size; i++) {
generate_random(population[i], num_dims, num_bits_per_dimension);
compute_fitness(&population[i], num_dims, num_bits_per_dimension, min_x, max_x);
}
qsort((void *)population, pop_size, sizeof(population[0]), sort_function);
printf("generation 0\n");
// print the best from generation 0
print_chromosome(&population[0], num_dims, num_bits_per_dimension, min_x, max_x);
for (int g = 1; g < num_gens; g++) {
for (int k = 0; k < pop_size; k += 2) {
// choose the parents using binary tournament
int r1 = tournament_selection(2, population, pop_size);
int r2 = tournament_selection(2, population, pop_size);
// crossover
double p = rand() / double(RAND_MAX);
if (p < pcross)
one_cut_point_crossover(population[r1], population[r2], offspring1, offspring2, num_dims, num_bits_per_dimension);
else {
copy_individual(&offspring1, population[r1], num_dims, num_bits_per_dimension);
copy_individual(&offspring2, population[r2], num_dims, num_bits_per_dimension);
}
// mutate the result and compute its fitness
mutation(offspring1, num_dims, num_bits_per_dimension, pm);
compute_fitness(&offspring1, num_dims, num_bits_per_dimension, min_x, max_x);
mutation(offspring2, num_dims, num_bits_per_dimension, pm);
compute_fitness(&offspring2, num_dims, num_bits_per_dimension, min_x, max_x);
// are offspring better than the worst ?
if (offspring1.fitness < population[pop_size - 1].fitness) {
copy_individual(&population[pop_size - 1], offspring1, num_dims, num_bits_per_dimension);
qsort((void *)population, pop_size, sizeof(population[0]), sort_function);
}
if (offspring2.fitness < population[pop_size - 1].fitness) {
copy_individual(&population[pop_size - 1], offspring2, num_dims, num_bits_per_dimension);
qsort((void *)population, pop_size, sizeof(population[0]), sort_function);
}
}
printf("generation %d\n", g);
print_chromosome(&population[0], num_dims, num_bits_per_dimension, min_x, max_x);
}
// free memory
free(offspring1.x);
free(offspring2.x);
for (int i = 0; i < pop_size; i++)
free(population[i].x);
free(population);
}
