Titre
Memetic viability evolution for constrained optimization
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Auteur(s)
Maesani, A.
Auteure/Auteur
Iacca, G.
Auteure/Auteur
Floreano, D.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1941-0026
Statut éditorial
Publié
Date de publication
2016
Volume
20
Numéro
1
Première page
125
Dernière page/numéro d’article
144
Langue
anglais
Notes
7102737
Résumé
The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while covariance matrix adaptation evolution strategy (CMA-ES) is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from memetic computing, i.e., the harmonious combination of multiple units of algorithmic information, and viability evolution, an alternative abstraction of artificial evolution, to devise a novel approach for solving optimization problems with inequality constraints. Viability evolution emphasizes the elimination of solutions that do not satisfy viability criteria, which are defined as boundaries on objectives and constraints. These boundaries are adapted during the search to drive a population of local search units, based on CMAES, toward feasible regions. These units can be recombined by means of differential evolution operators. Of crucial importance for the performance of our method, an adaptive scheduler toggles between exploitation and exploration by selecting to advance one of the local search units and/or recombine them. The proposed algorithm can outperform several state-of-the-art methods on a diverse set of benchmark and engineering problems, both for quality of solutions and computational resources needed.
Sujets
PID Serval
serval:BIB_74DD33FA444F
Date de création
2016-02-01T10:23:11.145Z
Date de création dans IRIS
2025-05-21T00:31:25Z