public static void Run_Experiment__Normal(
string experiment_name,
MEL__Parent_Selection_Method <MEL_Rastrigin__Individual> parent_selection_method,
int number_of_dimensions
)
{
int initial_population_size = 100;
int number_of_iterations = 1_000_000;
double minimum_gene_value = -5.12;
double maximum_gene_value = 5.12;
double gene_value_range = maximum_gene_value - minimum_gene_value;
double mutation_rate = 0.05;
double mutation_step = mutation_rate * gene_value_range;
int num_subdivisions = 100;
List <int> data_save_points = new List <int>();
for (int i = 0; i < 10_000; i += 1_000)
{
data_save_points.Add(i);
}
for (int i = 10_000; i < 100_000; i += 10_000)
{
data_save_points.Add(i);
}
for (int i = 100_000; i <= 1_000_000; i += 100_000)
{
data_save_points.Add(i);
}
List <int> random_seeds = new List <int>();
for (int i = 1000; i < 1100; i++)
{
random_seeds.Add(i);
}
List <int> feature_tables_png_save_points = new List <int>()
{
number_of_iterations
};
MEL_Experiment_Runner.Run_Experiment(
experiment_name,
random_seeds,
new MEL__Operator_Settings <MEL_Rastrigin__Individual>(
new MEL_Rastrigin__Individual_Generator(
number_of_dimensions,
minimum_gene_value,
maximum_gene_value
),
new MEL_Rastrigin__Individual_Mutator(
mutation_step,
minimum_gene_value,
maximum_gene_value
)
),
new MEL__Evaluation_Settings <MEL_Rastrigin__Individual>(
new MEL_Rastrigin__Fitness__Evaluation_Method(),
new MEL_Rastrigin__Feature_1__Evaluation_Method(),
new MEL_Rastrigin__Feature_2__Evaluation_Method(),
minimum_gene_value,
maximum_gene_value,
num_subdivisions,
minimum_gene_value,
maximum_gene_value,
num_subdivisions
),
parent_selection_method,
initial_population_size,
number_of_iterations,
data_save_points,
feature_tables_png_save_points,
data_save_points,
data_save_points
);
}
public static void Run_Experiment(
string experiment_name,
MEL__Parent_Selection_Method <MEL_ArmRepertoire__Individual> parent_selection_method,
int number_of_dimensions
)
{
// experiment - specific - settings
int number_of_iterations = 1_000_000;
List <int> data_save_points = new List <int>();
for (int i = 0; i < 10_000; i += 1_000)
{
data_save_points.Add(i);
}
for (int i = 10_000; i < 100_000; i += 10_000)
{
data_save_points.Add(i);
}
for (int i = 100_000; i <= 1_000_000; i += 100_000)
{
data_save_points.Add(i);
}
List <int> feature_tables_png_save_points = new List <int>()
{
number_of_iterations
};
// general settings
double individual_gene_min_value = -Math.PI;
double individual_gene_max_value = Math.PI;
double uniform_mutation_rate = 0.05;
double gene_value_range = individual_gene_max_value - individual_gene_min_value;
double mutation_step = uniform_mutation_rate * gene_value_range;
double feature_1_min_value = -1.0;
double feature_1_max_value = 1.0;
int feature_1_subdivisions = 100;
double feature_2_min_value = -1.0;
double feature_2_max_value = 1.0;
int feature_2_subdivisions = 100;
int initial_population = 100;
List <int> random_seeds = new List <int>();
for (int i = 1000; i < 1100; i++)
{
random_seeds.Add(i);
}
MEL_Experiment_Runner.Run_Experiment(
experiment_name,
random_seeds,
new MEL__Operator_Settings <MEL_ArmRepertoire__Individual>(
new MEL_ArmRepertoire__Individual_Generator(
number_of_dimensions,
individual_gene_min_value,
individual_gene_max_value
),
new MEL_ArmRepertoire__Individual_Mutator__Mutation_Step__Uniform_Rate(
mutation_step,
individual_gene_min_value,
individual_gene_max_value
)
),
new MEL__Evaluation_Settings <MEL_ArmRepertoire__Individual>(
new MEL_ArmRepertoire__Individual_Evaluator__Fitness(),
new MEL_ArmRepertoire__Individual_Evaluator__Feature_1(),
new MEL_ArmRepertoire__Individual_Evaluator__Feature_2(),
feature_1_min_value,
feature_1_max_value,
feature_1_subdivisions,
feature_2_min_value,
feature_2_max_value,
feature_2_subdivisions
),
parent_selection_method,
initial_population,
number_of_iterations,
data_save_points,
feature_tables_png_save_points,
data_save_points,
data_save_points
);
}
