Performance evaluation and population reduction for a self adaptive hybrid genetic algorithm (SAHGA)

Authors:
Felipe P. Espinoza;Barbara S. Minsker;David E. Goldberg
Affiliations:
University of Illinois, Department of Civil and Environmental Engineering, Urbana, IL;University of Illinois, Department of Civil and Environmental Engineering, Urbana, IL;University of Illinois, Department of General Engineering, Urbana, IL
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Year:
2003

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Abstract

This paper examines the effects of local search on hybrid genetic algorithm performance and population sizing. It compares the performance of a self-adaptive hybrid genetic algorithm (SAHGA) to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on eight different test functions, including unimodal, multimodal and constrained optimization problems. The results show that the hybrid genetic algorithm substantially reduces required population sizes because of the reduction in population variance. The adaptive nature of the SAHGA algorithm together with the reduction in population size allow for faster solution of the test problems without sacrificing solution quality.