//---------------------------------------------------------------------------
void start_steady_state(t_parameters ¶ms, t_graph *training_graphs, int num_training_graphs, t_graph *validation_graphs, int num_validation_graphs, int num_variables, int num_procs, int current_proc_id)
{
int size_to_send = params.code_length * sizeof(t_code3) + params.num_constants * 20 + 20;
char *s_source = new char[size_to_send];
char *s_dest = new char[size_to_send];
t_chromosome receive_chromosome;
allocate_chromosome(receive_chromosome, params);
// a steady state model -
// Newly created inviduals replace the worst ones (if the offspring are better) in the same (sub) population.
// allocate memory for all sub populations
t_chromosome **sub_populations; // an array of sub populations
sub_populations = new t_chromosome*[params.num_sub_populations];
for (int p = 0; p < params.num_sub_populations; p++) {
sub_populations[p] = new t_chromosome[params.sub_population_size];
for (int i = 0; i < params.sub_population_size; i++)
allocate_chromosome(sub_populations[p][i], params); // allocate each individual in the subpopulation
}
#ifdef USE_THREADS
// allocate memory
double** vars_values = new double*[params.num_threads];
for (int t = 0; t < params.num_threads; t++)
vars_values[t] = new double[num_variables];
// an array of threads. Each sub population is evolved by a thread
std::thread **mep_threads = new std::thread*[params.num_threads];
// we create a fixed number of threads and each thread will take and evolve one subpopulation, then it will take another one
std::mutex mutex;
// we need a mutex to make sure that the same subpopulation will not be evolved twice by different threads
#else
double* vars_values = new double[num_variables];
#endif
t_chromosome best_validation;
allocate_chromosome(best_validation, params);
double best_validation_fitness = DBL_MAX;
// evolve for a fixed number of generations
for (int generation = 0; generation < params.num_generations; generation++) { // for each generation
#ifdef USE_THREADS
int current_subpop_index = 0;
for (int t = 0; t < params.num_threads; t++)
mep_threads[t] = new std::thread(evolve_one_subpopulation, ¤t_subpop_index, &mutex, sub_populations, generation, ¶ms, training_graphs, num_training_graphs, num_variables, vars_values[t]);
for (int t = 0; t < params.num_threads; t++) {
mep_threads[t]->join();
delete mep_threads[t];
}
#else
//for (int p = 0; p < params.num_sub_populations; p++)
int p = 0;
evolve_one_subpopulation(&p, sub_populations, generation, ¶ms, training_graphs, num_training_graphs, num_variables, vars_values);
#endif
// find the best individual
int best_individual_subpop_index = 0; // the index of the subpopulation containing the best invidual
for (int p = 1; p < params.num_sub_populations; p++)
if (sub_populations[p][0].fitness < sub_populations[best_individual_subpop_index][0].fitness)
best_individual_subpop_index = p;
double *partial_values_array = new double[params.code_length];
sub_populations[best_individual_subpop_index][0].simplify(params.code_length);
double current_validation_fitness = compute_fitness(sub_populations[best_individual_subpop_index][0], validation_graphs, num_validation_graphs, num_variables, vars_values, partial_values_array);
if (!generation || generation && current_validation_fitness < best_validation_fitness) {
best_validation_fitness = current_validation_fitness;
copy_individual(best_validation, sub_populations[best_individual_subpop_index][0], params);
}
delete[] partial_values_array;
printf("proc_id=%d, generation=%d, best training =%lf best validation =%lf\n", current_proc_id, generation, sub_populations[best_individual_subpop_index][0].fitness, best_validation_fitness);
if (current_proc_id == 0) {
FILE* f = fopen("tst_log.txt", "a");
fprintf(f, "proc_id=%d, generation=%d, best=%lf\n", current_proc_id, generation, sub_populations[best_individual_subpop_index][0].fitness);
fclose(f);
}
// now copy one individual from one population to the next one.
// the copied invidual will replace the worst in the next one (if is better)
for (int p = 0; p < params.num_sub_populations; p++) {
int k = rand() % params.sub_population_size;// the individual to be copied
// replace the worst in the next population (p + 1) - only if is better
int index_next_pop = (p + 1) % params.num_sub_populations; // index of the next subpopulation (taken in circular order)
if (sub_populations[p][k].fitness < sub_populations[index_next_pop][params.sub_population_size - 1].fitness) {
copy_individual(sub_populations[index_next_pop][params.sub_population_size - 1], sub_populations[p][k], params);
qsort((void *)sub_populations[index_next_pop], params.sub_population_size, sizeof(sub_populations[0][0]), sort_function);
}
}
#ifdef USE_MPI
// here I have to copy few individuals from one process to another process
for (int i = 0; i < 1; i++) {
int source_sub_population_index = rand() % params.num_sub_populations;
int chromosome_index = rand() % params.sub_population_size;
int tag = 0;
sub_populations[source_sub_population_index][chromosome_index].to_string(s_dest, params.code_length, params.num_constants);
MPI_Send((void*)s_dest, size_to_send, MPI_CHAR, (current_proc_id + 1) % num_procs, tag, MPI_COMM_WORLD);
MPI_Status status;
int flag;
MPI_Iprobe(!current_proc_id ? num_procs - 1 : current_proc_id - 1, tag, MPI_COMM_WORLD, &flag, &status);
if (flag) {
MPI_Recv(s_source, size_to_send, MPI_CHAR, !current_proc_id ? num_procs - 1 : current_proc_id - 1, tag, MPI_COMM_WORLD, &status);
receive_chromosome.from_string(s_source, params.code_length, params.num_constants);
int dest_sub_population_index = rand() % params.num_sub_populations;
if (receive_chromosome.fitness < sub_populations[dest_sub_population_index][params.sub_population_size - 1].fitness) {
copy_individual(sub_populations[dest_sub_population_index][params.sub_population_size - 1], receive_chromosome, params);
qsort((void *)sub_populations[dest_sub_population_index], params.sub_population_size, sizeof(sub_populations[0][0]), sort_function);
}
}
}
#endif
}
#ifdef USE_THREADS
delete[] mep_threads;
#endif
// print best t_chromosome
// any of them can be printed because if we allow enough generations, the populations will become identical
print_chromosome(best_validation, params, num_variables);
best_validation.to_file_simplified("best.txt", params.code_length, params.num_constants);
// free memory
for (int p = 0; p < params.num_sub_populations; p++) {
for (int i = 0; i < params.sub_population_size; i++)
delete_chromosome(sub_populations[p][i]);
delete[] sub_populations[p];
}
delete[] sub_populations;
#ifdef USE_THREADS
for (int t = 0; t < params.num_threads; t++)
delete[] vars_values[t];
delete[] vars_values;
#endif
delete[] s_dest;
delete[] s_source;
delete_chromosome(receive_chromosome);
delete_chromosome(best_validation);
}
int main()
{
BT bt;
bt=get_memaddr("core");
int i;
/* testing for the data structure BST SCORE_SHEET*/
CHROMO c1,c2,c3,c4,c5;
sprintf(c1.filename,"winkeyd.exe");
c1.score=6;
sprintf(c2.filename,"this is c2");
c2.score=8;
sprintf(c3.filename,"x1.mp3");
c3.score=3;
sprintf(c4.filename,"x2.mp3");
c4.score=40;
sprintf(c5.filename,"x3.mp3");
c5.score=60;
SCORE_SHEET *ss=init_list();
ss=insert_chromosome(ss,c1);
ss=insert_chromosome(ss,c2);
ss=insert_chromosome(ss,c3);
ss=insert_chromosome(ss,c4);
ss=insert_chromosome(ss,c5);
printf("the sum of scores is: %d\n",sum_of_scores(ss));
ss=delete_chromosome(ss,c1);
/*selection of individual*/
//print_list(ss);
free(ss);
char **files;
int noFiles=3;
files=(char **)malloc(noFiles*sizeof(char *));
for(i=0;i<noFiles;i++)
files[i]=(char *)malloc(20*sizeof(char));
strcpy(files[0],"x1.mp3");
strcpy(files[1],"x2.mp3");
strcpy(files[2],"x3.mp3");
POPULATION pop=initialize("avgscan","mp3",files,noFiles,bt);
print_list(pop);
return 0;
}
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);
}
