public MEL__Algorithm(
MEL__Operator_Settings <T> operator_settings,
MEL__Evaluation_Settings <T> evaluation_settings,
MEL__Parent_Selection_Method <T> parent_selection_method
)
{
this.operator_settings = operator_settings;
this.evaluation_settings = evaluation_settings;
this.parent_selection_method = parent_selection_method;
this.state = new MEL__State <T>(
evaluation_settings.feature_1_subdivisions,
evaluation_settings.feature_2_subdivisions
);
}
public static void Run_Experiment <T>(
string experiment_name,
List <int> random_seeds, // implies number of repetitions...
MEL__Operator_Settings <T> operator_settings,
MEL__Evaluation_Settings <T> eval_settings,
MEL__Parent_Selection_Method <T> selection_method,
int initial_population,
int total_num_iterations,
List <int> iterations_for_feature_tables_csv,
List <int> iterations_for_feature_tables_png,
List <int> iterations_for_data_logging,
List <int> iterations_for_console_logging
)
where T : MEL__Individual
{
// prepare paths etc...
string general_output_folder = Path.Combine(
Directory.GetCurrentDirectory(),
"output"
);
if (IO_Utilities.Folder_Exists(general_output_folder) == false)
{
// create the general output folder
IO_Utilities.CreateFolder(general_output_folder);
}
string this_experiment__output_folder = Path.Combine(
general_output_folder,
experiment_name + "___" + DateTime.UtcNow.Ticks.ToString()
);
if (IO_Utilities.Folder_Exists(this_experiment__output_folder) == false)
{
// create this experiment's folder
IO_Utilities.CreateFolder(this_experiment__output_folder);
}
string data_logging_file_path = Path.Combine(
this_experiment__output_folder,
experiment_name + "_data.csv"
);
if (IO_Utilities.File_Exists(data_logging_file_path) == false)
{
IO_Utilities.Create_File(data_logging_file_path, false, false);
}
// create and save the data header...
string data_header = Map_Elites__Data_Header();
IO_Utilities.Append_To_File(data_logging_file_path, data_header);
MEL__Algorithm <T> map_elites = new MEL__Algorithm <T>(
operator_settings,
eval_settings,
selection_method
);
Save_Experiment_Settings(
this_experiment__output_folder,
experiment_name,
map_elites
);
foreach (var random_seed in random_seeds)
{
// initialize the random numbers generator...
Random randomness_provider = new Random(random_seed);
map_elites = new MEL__Algorithm <T>(
operator_settings,
eval_settings,
selection_method
);
// generate the initial population
map_elites.Generate_Initial_Population(randomness_provider, initial_population);
// save the data at this stage, before any operation...
Save_Feature_Tables_CSV(
this_experiment__output_folder,
random_seed,
0,
map_elites
);
Save_Feature_Tables_PNG(
this_experiment__output_folder: this_experiment__output_folder,
random_seed: random_seed,
current_iteration: 0,
map_elites: map_elites,
min_fitness: 0.0,
max_fitness: 1.0
);
string data_row = Map_Elites__Data_Row(random_seed, 0, map_elites);
IO_Utilities.Append_To_File(data_logging_file_path, data_row);
Console_Log(experiment_name, random_seed, 0);
for (int current_iteration = 1; current_iteration <= total_num_iterations; current_iteration++)
{
// advance algorithm
map_elites.Select_And_Mutate_Individual(randomness_provider);
// perhaps, save data
if (iterations_for_feature_tables_csv.Contains(current_iteration))
{
Save_Feature_Tables_CSV(
this_experiment__output_folder,
random_seed,
current_iteration,
map_elites
);
}
if (iterations_for_feature_tables_png.Contains(current_iteration))
{
Save_Feature_Tables_PNG(
this_experiment__output_folder: this_experiment__output_folder,
random_seed: random_seed,
current_iteration: current_iteration,
map_elites: map_elites,
min_fitness: 0.0,
max_fitness: 1.0
);
}
if (iterations_for_data_logging.Contains(current_iteration))
{
data_row = Map_Elites__Data_Row(random_seed, current_iteration, map_elites);
IO_Utilities.Append_To_File(data_logging_file_path, data_row);
}
if (iterations_for_console_logging.Contains(current_iteration))
{
Console_Log(experiment_name, random_seed, current_iteration);
}
}
}
}