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JACIII Vol.9 No.5 pp. 549-555
doi: 10.20965/jaciii.2005.p0549
(2005)

Paper:

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
Published:
September 20, 2005
Keywords:
differential evolution, global optimization, generalized generation gap model, parent-centric recombination
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.

Cite this article as:
Jani Rönkkönen, Saku Kukkonen, and Jouni Lampinen, “A Comparison of Differential Evolution and Generalized Generation Gap Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.9, No.5, pp. 549-555, 2005.
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