Convergence time for the linkage learning genetic algorithm

Ying-Ping Chen*, David E. Goldberg

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper identifies the sequential behavior of the linkage learning genetic algorithm (LLGA), introduces the tightness time model for a single building block, and develops the connection between sequential behavior and the tightness time model. By integrating the first-building-block model based on sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is then constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by LLGA when solving uniformly scaled problems.

Original languageEnglish
Title of host publicationProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Pages39-46
Number of pages8
DOIs
StatePublished - 13 Sep 2004
EventProceedings of the 2004 Congress on Evolutionary Computation, CEC2004 - Portland, OR, United States
Duration: 19 Jun 200423 Jun 2004

Publication series

NameProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Volume1

Conference

ConferenceProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
CountryUnited States
CityPortland, OR
Period19/06/0423/06/04

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    Chen, Y-P., & Goldberg, D. E. (2004). Convergence time for the linkage learning genetic algorithm. In Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 (pp. 39-46). (Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004; Vol. 1). https://doi.org/10.1109/CEC.2004.1330835