research-article

Effects of discrete hill climbing on model building forestimation of distribution algorithms

Authors:

Wei-Che ChienAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 367 - 374

https://doi.org/10.1145/2463372.2463418

Published: 06 July 2013 Publication History

Abstract

Hybridization of global and local searches is a well-known technique for optimization algorithms. Hill climbing is one of the local search methods. On estimation of distribution algorithms (EDAs), hill climbing strengthens the signals of dependencies on correlated variables and improves the quality of model building, which reduces the required population size and convergence time. However, hill climbing also consumes extra computational time. In this paper, analytical models are developed to investigate the effects of combining two different hill climbers with the extended compact genetic algorithm and the dependency structure matrix genetic algorithm. By using the one-max problem and the 5-bit non-overlapping trap problem as the test problems, the performances of different hill climbers are compared. Both analytical models and experiments reveal that the greedy hill climber reduces the number of function evaluations for EDAs to find the global optimum.

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Cited By

Yu JChen IYu TBosman P(2017)A diversity preservation scheme for DSMGA-II to conquer the hierarchical difficultyProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071253(841-848)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071253
Hsu SYu TEsparcia-Alcázar A(2015)Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back MixingProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754737(519-526)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754737
Martins JDelbem AArnold D(2014)Multimodality and the linkage-learning difficulty of additively separable functionsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598281(365-372)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598281

Index Terms

Effects of discrete hill climbing on model building forestimation of distribution algorithms

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cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

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Published: 06 July 2013

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GECCO '13

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GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Yu JChen IYu TBosman P(2017)A diversity preservation scheme for DSMGA-II to conquer the hierarchical difficultyProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071253(841-848)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071253
Hsu SYu TEsparcia-Alcázar A(2015)Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back MixingProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754737(519-526)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754737
Martins JDelbem AArnold D(2014)Multimodality and the linkage-learning difficulty of additively separable functionsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598281(365-372)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598281

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