Evolutionary Multimodal Optimization Revisited^*

Rajeev Kumar¹ and Peter Rockett²

¹Department of Computer Science & Engineering
Indian Institute of Technology
Kharagpur 721 302, India
rkumar@cse.iitkgp.ernet.in

²Department of Electronic & Electrical Engineering
Mappin St
University of Sheffield
Sheffield S1 3JD, England
p.rockett@sheffield.ac.uk

Abstract. We revisit a class of multimodal function optimizations using evolutionary algorithms reformulated into a multiobjective framework where previous implementations have needed niching/sharing to ensure diversity. In this paper, we use a steady-state multiobjective algorithm which preserves diversity without niching to produce diverse sampling of the Pareto-front with significantly lower computational effort.

^*Partially supported by the Ministry of Human Resource Development, Government of India

LNCS 2724, p. 1592 ff.

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