Paper:
Language: English:

A Comparison of Differential Evolution and Generalized Generation Gap Model

Jani Rönkkönen, Saku Kukkonen, and Jouni Lampinen

Laboratory of Information Processing, Department of Information Technology, Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland

Received: December 4, 2004

Accepted: May 19, 2005

Keywords: differential evolution, global optimization, generalized generation gap model, parent-centric recombination

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.9, No.5 pp. 549-555, 2005

Abstract

We compared two floating-point-encoded evolutionary algorithms (EA) – differential evolution (DE) and the generalized generation gap (G3) – using a set of problems with different characteristics. G3 is reported to offer superior performance with unimodal functions, which are, however, often solved more efficiently using derivative-based optimization for example and it is interesting to know, how these algorithms perform in multimodal global optimization problems.
Our results suggest that G3 converges fast but is prone to converge prematurely rather than finding the global optimum in high-dimensional multimodal problems. DE, in contrast, appears to handle multimodal problems better but cannot match convergence speed of G3 in unimodal problems.

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