EDAspy.optimization.multivariate package

Submodules

EDAspy.optimization.multivariate.egna module

class EDAspy.optimization.multivariate.egna.EGNA(size_gen: int, max_iter: int, dead_iter: int, n_variables: int, landscape_bounds: tuple, alpha: float = 0.5, elite_factor: float = 0.4, disp: bool = True)[source]

Bases: EDA

Estimation of Gaussian Networks Algorithm. This type of Estimation-of-Distribution Algorithm uses a Gaussian Bayesian Network from where new solutions are sampled. This multivariate probabilistic model is updated in each iteration with the best individuals of the previous generation.

EGNA [1] has shown to improve the results for more complex optimization problem compared to the univariate EDAs that can be found implemented in this package. Different modifications have been done into this algorithm such as in [2] where some evidences are input to the Gaussian Bayesian Network in order to restrict the search space in the landscape.

Example

This example uses some very well-known benchmarks from CEC14 conference to be solved using an Estimation of Gaussian Networks Algorithm (EGNA).

from EDAspy.optimization import EGNA
from EDAspy.benchmarks import ContinuousBenchmarkingCEC14

benchmarking = ContinuousBenchmarkingCEC14(10)

egna = EGNA(size_gen=300, max_iter=100, dead_iter=20, n_variables=10,
            landscape_bounds=(-60, 60))

eda_result = egna.minimize(benchmarking.cec14_4, True)

References

[1]: Larrañaga, P., & Lozano, J. A. (Eds.). (2001). Estimation of distribution algorithms: A new tool for evolutionary computation (Vol. 2). Springer Science & Business Media.

[2]: Vicente P. Soloviev, Pedro Larrañaga and Concha Bielza (2022). Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables. Journal of Combinatorial Optimization.

EDAspy.optimization.multivariate.emna module

class EDAspy.optimization.multivariate.emna.EMNA(size_gen: int, max_iter: int, dead_iter: int, n_variables: int, landscape_bounds: tuple, alpha: float = 0.5, elite_factor: float = 0.4, disp: bool = True, lower_bound: float = 0.5, upper_bound: float = 100)[source]

Bases: EDA

Estimation of Multivariate Normal Algorithm (EMNA) [1] is a multivariate continuous EDA in which no probabilistic graphical models are used during runtime. In each iteration the new solutions are sampled from a multivariate normal distribution built from the elite selection of the previous generation.

In this implementation, as in EGNA, the algorithm is initialized from a uniform sampling in the landscape bound you input in the constructor of the algorithm. If a different initialization_models is desired, then you can override the class and this specific method.

This algorithm is widely used in the literature and compared for different optimization tasks with its competitors in the EDAs multivariate continuous research topic.