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Is the meta-EA a viable optimization method?

Authors:

AKM Khaled Ahsan TalukderAuthors Info & Claims

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

Pages 1533 - 1540

https://doi.org/10.1145/2463372.2465806

Published: 06 July 2013 Publication History

Abstract

Meta-evolutionary algorithms have long been proposed as an approach to automatically discover good parameter settings to use in later optimization runs. In this paper we instead ask whether a meta-evolutionary algorithm makes sense as an optimizer in its own right. That is, we're not interested in the resulting parameter settings, but only in the final result. As it so happens, this use of meta-EAs make sense in the context of large numbers of parallel runs, particularly in massive distributed scenarios. A primary issue facing meta-EAs is the stochastic nature of the meta-level fitness function. We consider whether this poses a challenge to establishing a gradient in the meta-level search space, and to what degree multiple tests are helpful in smoothing the noise. We discuss the nature of the meta-level search space and its impact on local optima, then examine the degree to which exploitation can be applied. We find that meta-EAs perform well as optimizers, and very surprisingly that they do best with only a single test. More exploitation appears to reduce performance, but only slightly.

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Cited By

Indu MShunmuga Velayutham C(2021)Towards Grammatical Evolution-Based Automated Design of Differential Evolution AlgorithmCongress on Intelligent Systems10.1007/978-981-33-6984-9_27(329-340)Online publication date: 2-Jun-2021
https://doi.org/10.1007/978-981-33-6984-9_27
He YAranha CSakurai T(2020)Parameter Evolution Self-Adaptive Strategy and Its Application for Cuckoo SearchBioinspired Optimization Methods and Their Applications10.1007/978-3-030-63710-1_5(56-68)Online publication date: 16-Nov-2020
https://doi.org/10.1007/978-3-030-63710-1_5
Ugolotti RSani LCagnoni S(2019)What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?Mathematics10.3390/math70302327:3(232)Online publication date: 4-Mar-2019
https://doi.org/10.3390/math7030232
Show More Cited By

Index Terms

Is the meta-EA a viable optimization method?
1. Computing methodologies
  1. Artificial intelligence

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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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 06 July 2013

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GECCO '13

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SIGEVO

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

Indu MShunmuga Velayutham C(2021)Towards Grammatical Evolution-Based Automated Design of Differential Evolution AlgorithmCongress on Intelligent Systems10.1007/978-981-33-6984-9_27(329-340)Online publication date: 2-Jun-2021
https://doi.org/10.1007/978-981-33-6984-9_27
He YAranha CSakurai T(2020)Parameter Evolution Self-Adaptive Strategy and Its Application for Cuckoo SearchBioinspired Optimization Methods and Their Applications10.1007/978-3-030-63710-1_5(56-68)Online publication date: 16-Nov-2020
https://doi.org/10.1007/978-3-030-63710-1_5
Ugolotti RSani LCagnoni S(2019)What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?Mathematics10.3390/math70302327:3(232)Online publication date: 4-Mar-2019
https://doi.org/10.3390/math7030232
Scott ELuke SAuger AStützle T(2019)ECJ at 20Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326865(1391-1398)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326865
Luke S(2017)ECJ then and nowProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082467(1223-1230)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082467
Soria-Alcaraz JEspinal ASotelo-Figueroa M(2017)Evolvability Metric Estimation by a Parallel Perceptron for On-Line Selection Hyper-HeuristicsIEEE Access10.1109/ACCESS.2017.26994265(7055-7063)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2699426
Scott EBassett JEsparcia-Alcázar A(2015)Learning Genetic Representations for Classes of Real-Valued Optimization ProblemsProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2768460(1075-1082)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2768460
Soria Alcaraz JOchoa GCarpio MPuga HArnold D(2014)Evolvability metrics in adaptive operator selectionProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598220(1327-1334)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598220

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