Memetic viability evolution for constrained optimization

Détails

ID Serval
serval:BIB_74DD33FA444F
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Memetic viability evolution for constrained optimization
Périodique
IEEE Transactions on Evolutionary Computation
Auteur(s)
Maesani A., Iacca G., Floreano D.
ISSN
1941-0026
ISSN-L
1089-778X
Statut éditorial
Publié
Date de publication
2016
Volume
20
Numéro
1
Pages
125-144
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.
Mots-clé
Covariance matrices, Evolutionary computation, Memetics, Optimization, Search problems, Sociology, Constrained Optimization, Constrained optimization, Covariance Matrix Adaptation, Differential Evolution, Memetic Computing, Viability Evolution, covariance matrix adaptation, differential evolution (DE), memetic computing (MC), viability evolution
Web of science
Création de la notice
01/02/2016 11:23
Dernière modification de la notice
20/08/2019 14:32
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