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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, 2004Accepted:May 19, 2005Published: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:J. Rönkkönen, S. Kukkonen, and J. 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.Data files:
