A study of memetic search with multi-parent combination for UBQP

Authors:
Zhipeng Lü;Jin-Kao Hao;Fred Glover
Affiliations:
LERIA, Université d'Angers, Angers Cedex 01, France;LERIA, Université d'Angers, Angers Cedex 01, France;OptTek Systems, Inc.
Venue:
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Year:
2010

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Abstract

We present a multi-parent hybrid genetic–tabu algorithm (denoted by GTA) for the Unconstrained Binary Quadratic Programming (UBQP) problem, by incorporating tabu search into the framework of genetic algorithm. In this paper, we propose a new multi-parent combination operator for generating offspring solutions. A pool updating strategy based on a quality-and-distance criterion is used to manage the population. Experimental comparisons with leading methods for the UBQP problem on 25 large public instances demonstrate the efficacy of our proposed algorithm in terms of both solution quality and computational efficiency.