pyRVEA.Population

pyRVEA.Population.Population module

class pyRVEA.Population.Population.Population(problem: baseProblem, assign_type: str = 'RandomAssign', plotting: bool = True, *args)

Bases: object

Define the population.

add(new_pop: numpy.ndarray)

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

Parameters

new_pop (np.ndarray) – Decision variable values for new population.

append_individual(ind: numpy.ndarray)

Evaluate and add individual to the population.

Parameters

ind (np.ndarray) –

create_new_individuals(design: str = 'LHSDesign', pop_size: int = None, decision_variables=None)

Create, evaluate and add new individuals to the population. Initiate Plots.

The individuals can be created randomly, by LHS design, or can be passed by the user.

Parameters

  • design (str, optional) – Describe the method of creation of new individuals. “RandomDesign” creates individuals randomly. “LHSDesign” creates individuals using Latin hypercube sampling.

  • pop_size (int, optional) – Number of individuals in the population. If none, some default population size based on number of objectives is chosen.

  • decision_variables (numpy array or list, optional) – Pass decision variables to be added to the population.

eval_fitness()

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

evaluate_individual(ind: numpy.ndarray)

Evaluate individual.

Returns objective values, constraint violation, and fitness.

Parameters

ind (np.ndarray) –

evolve(EA: BaseEA = None, EA_parameters: dict = None) → Population

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)

hypervolume(ref_point)

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

Parameters

ref_point –

keep(indices: list)

Remove individuals from population which are not in “indices”.

Parameters

indices (list) – Indices of individuals to keep

mate()

Conduct crossover and mutation over the population.

Conduct simulated binary crossover and bounded polunomial mutation.

non_dominated()

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

plot_init_()

Initialize animation objects. Return figure

plot_objectives(iteration: int)

Plot the objective values of individuals in notebook. This is a hack.

Parameters

iteration (int) – Iteration count.

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.)