Evolutionary Multimodal Optimization Revisited

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. Multimodal optimization (MMO) and multiobjective optimization (MOO) are two classes of optimizations requiring multiple (near-)optimal solutions: having found a solution set, a user makes a selection from the (hopefully) diverse options. In this context, niching/sharing techniques have been commonly employed to ensure a diverse solution set although such techniques work the best when one has a priori knowledge of the problem. In most real-problems, the analytical form is unknown and so picking good niche parameters is problematic. Consequently, most of the work related to M...

• Type: Conference
• Year: 2003
• Where: GECCO
• Authors: Rajeev Kumar, Peter Rockett
• Keywords: GECCO 2003, MOO Problems, Solution Set, Steady-state Multiobjective Algorithm