desdeo_emo.EAs.NSGAIII
Module Contents
Classes
Python Implementation of NSGA-III. Based on the pymoo package. |
- class desdeo_emo.EAs.NSGAIII.NSGAIII(problem: desdeo_problem.MOProblem, population_size: int = None, population_params: Dict = None, n_survive: int = None, initial_population: desdeo_emo.population.Population.Population = None, lattice_resolution: int = None, selection_type: str = None, interact: bool = False, use_surrogates: bool = False, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, keep_archive: bool = False, save_non_dominated: bool = False)[source]
Bases:
desdeo_emo.EAs.BaseEA.BaseDecompositionEA
Python Implementation of NSGA-III. Based on the pymoo package.
Most of the relevant code is contained in the super class. This class just assigns the NSGAIII selection operator to BaseDecompositionEA.
- Parameters:
problem (MOProblem) – The problem class object specifying the details of the problem.
population_size (int, optional) – The desired population size, by default None, which sets up a default value of population size depending upon the dimensionaly of the problem.
population_params (Dict, optional) – The parameters for the population class, by default None. See desdeo_emo.population.Population for more details.
initial_population (Population, optional) – An initial population class, by default None. Use this if you want to set up a specific starting population, such as when the output of one EA is to be used as the input of another.
lattice_resolution (int, optional) – The number of divisions along individual axes in the objective space to be used while creating the reference vector lattice by the simplex lattice design. By default None
selection_type (str, optional) – One of [“mean”, “optimistic”, “robust”]. To be used in data-driven optimization. To be used only with surrogate models which return an “uncertainity” factor. Using “mean” is equivalent to using the mean predicted values from the surrogate models and is the default case. Using “optimistic” results in using (mean - uncertainity) values from the the surrogate models as the predicted value (in case of minimization). It is (mean + uncertainity for maximization). Using “robust” is the opposite of using “optimistic”.
a_priori (bool, optional) – A bool variable defining whether a priori preference is to be used or not. By default False
interact (bool, optional) – A bool variable defining whether interactive preference is to be used or not. By default False
n_iterations (int, optional) – The total number of iterations to be run, by default 10. This is not a hard limit and is only used for an internal counter.
n_gen_per_iter (int, optional) – The total number of generations in an iteration to be run, by default 100. This is not a hard limit and is only used for an internal counter.
total_function_evaluations (int, optional) – Set an upper limit to the total number of function evaluations. When set to zero, this argument is ignored and other termination criteria are used.