desdeo_emo.population.Population_old

Module Contents

Classes

Population

Define the population.

class desdeo_emo.population.Population_old.Population(problem: desdeo_problem.MOProblem, assign_type: str = 'RandomDesign', pop_size=None, recombination_type=None, crossover_type='simulated_binary_crossover', mutation_type='bounded_polynomial_mutation', *args)

Define the population.

add(new_pop: list)

Evaluate and add individuals to the population. Update ideal and nadir point.

Parameters:

new_pop (list) – Decision variable values for new population.

append_individual(ind: numpy.ndarray)

Evaluate and add individual to the population.

Parameters:

ind (np.ndarray) –

evaluate_individual(ind: numpy.ndarray)

Evaluate individual.

Returns objective values, constraint violation, and fitness.

Parameters:

ind (np.ndarray) –

eval_fitness(obj)

Calculate fitness based on objective values. Fitness = obj if minimized.

update_fitness()

Include or exclude objectives from fitness calculation. Problem.minimize should be a list of booleans of same length as the number of objectives.

delete(indices, preserve=False)

Remove from population individuals which are in indices if preserve=False, otherwise preserve them and remove all others.

Parameters:

• indices (array_like) – Indices of individuals to keep or delete.

• preserve (bool) – Whether to delete individuals at indices from current population, or preserve them and delete others.

evolve(EA: BaseEA = None, ea_parameters: dict = None)

Evolve the population with interruptions.

Evolves the population based on the EA sent by the user.

Parameters:

• EA ("BaseEA") – Should be a derivative of BaseEA (Default value = None)

• ea_parameters (dict) – Contains the parameters needed by EA (Default value = None)

mate(mating_pop=None, params=None)

Conduct crossover and mutation over the population.

plot_init_()

Initialize animation object. Return figure

plot_objectives(iteration: int = None)

Plot the objective values of individuals.

Parameters:

iteration (int) – Iteration count.

plot_pareto(name, show_all=False)

Plot the pareto front. REMOVE THIS IN THE FUTURE.

Parameters:

• name (str) – Name to append to the plot filename.

• show_all (bool) – Show all solutions, including those not on the pareto front.

hypervolume(ref_point)

Calculate hypervolume. Uses package pygmo. Add checks to prevent errors.

Parameters:

ref_point –

non_dominated()

Fix this. check if nd2 and nds mean the same thing

update_ideal_and_nadir(new_objective_vals: list = None)

Updates self.ideal and self.nadir in the fitness space.

Uses the entire population if new_objective_vals is none.

Parameters:

new_objective_vals (list, optional) – Objective values for a newly added individual (the default is None, which calculated the ideal and nadir for the entire population.)