desdeo_emo.population.Population_old
¶
Module Contents¶
Classes¶
Define the population. |
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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)[source]¶ Define the population.
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add
(self, new_pop: list)[source]¶ Evaluate and add individuals to the population. Update ideal and nadir point.
- Parameters
new_pop (list) – Decision variable values for new population.
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append_individual
(self, ind: numpy.ndarray)[source]¶ Evaluate and add individual to the population.
- Parameters
ind (np.ndarray) –
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evaluate_individual
(self, ind: numpy.ndarray)[source]¶ Evaluate individual.
Returns objective values, constraint violation, and fitness.
- Parameters
ind (np.ndarray) –
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eval_fitness
(self, obj)[source]¶ Calculate fitness based on objective values. Fitness = obj if minimized.
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update_fitness
(self)[source]¶ Include or exclude objectives from fitness calculation. Problem.minimize should be a list of booleans of same length as the number of objectives.
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delete
(self, indices, preserve=False)[source]¶ 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.
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evolve
(self, EA: BaseEA = None, ea_parameters: dict = None)[source]¶ 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)
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mate
(self, mating_pop=None, params=None)[source]¶ Conduct crossover and mutation over the population.
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plot_objectives
(self, iteration: int = None)[source]¶ Plot the objective values of individuals.
- Parameters
iteration (int) – Iteration count.
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plot_pareto
(self, name, show_all=False)[source]¶ 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.
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hypervolume
(self, ref_point)[source]¶ Calculate hypervolume. Uses package pygmo. Add checks to prevent errors.
- Parameters
ref_point –
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update_ideal_and_nadir
(self, new_objective_vals: list = None)[source]¶ 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.)
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