Title: Convergence time for the linkage learning genetic algorithm
Authors: Chen, YP
Goldberg, DE
資訊工程學系
Department of Computer Science
Keywords: genetic algorithms;genetic linkage;linkage learning;linkage learning genetic algorithm;sequential behavior;tightness time;convergence time
Issue Date: 1-Sep-2005
Abstract: This paper identifies the sequential behavior of the linkage learning genetic algorithm, introduces the tightness time model for a single building block, and develops the connection between the sequential behavior and the tightness time model. By integrating the first-building-block model based on the sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by the linkage learning genetic algorithm when solving uniformly scaled problems.
URI: http://dx.doi.org/10.1162/1063656054794806
http://hdl.handle.net/11536/13352
ISSN: 1063-6560
DOI: 10.1162/1063656054794806
Journal: EVOLUTIONARY COMPUTATION
Volume: 13
Issue: 3
Begin Page: 279
End Page: 302
Appears in Collections:Articles


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