A multi-objective meta-model assisted memetic algorithm with non gradient-based local search

Authors:
Saúl Zapotecas Martínez;Carlos A. Coello Coello
Affiliations:
CINVESTAV-IPN, México, DF, Mexico;CINVESTAV-IPN, México, DF, Mexico
Venue:
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Year:
2010

Citing 3
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Abstract

In this paper, we present an approach in which a local search mechanism is coupled to a multi-objective evolutionary algorithm. The local search mechanism is assisted by a meta-model based on support vector machines. Such a mechanism consists of two phases: the first one involves the use of an aggregating function which is defined by different weighted vectors. For the (scalar) optimization task involved, we adopt a non-gradient mathematical programming technique: the Hooke-Jeeves method. The second phase computes new solutions departing from those obtained in the first phase. The local search engine generates a set of solutions which are used in the evolutionary process of our algorithm. The preliminary results indicate that our proposed approach is quite promising.