Evolutionary multimodal optimization revisited

Authors:
Rajeev Kumar;Peter Rockett
Affiliations:
Department of Computer Science & Engineering, Indian Institute of Technology, Kharagpur, India;Department of Electronic & Electrical Engineering, Mappin St, University of Sheffield, Sheffield, England
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

Citing 2
Cited 1

Improved sampling of the Pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm

Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems

Evolutionary Computation

Genetic Algorithm with adaptive elitist-population strategies for multimodal function optimization

Applied Soft Computing

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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.