research-article

Controlling population size and mutation strength by Meta-ES under fitness noise

Authors:

Hans-Georg Beyer,

Michael HellwigAuthors Info & Claims

FOGA XII '13: Proceedings of the twelfth workshop on Foundations of genetic algorithms XII

Pages 11 - 24

https://doi.org/10.1145/2460239.2460242

Published: 16 January 2013 Publication History

Get Access

Abstract

This paper investigates strategy parameter control by Meta-ES using the noisy sphere model. The fitness noise considered is normally distributed with constant noise variance. An asymptotical analysis concerning the mutation strength and the population size is presented. It allows for the prediction of the Meta-ES dynamics. An expression describing the asymptotical growth of the normalized mutation strength is calculated. Finally, the theoretical results are evaluated empirically.

References

[1]

D. V. Arnold. Noisy Optimization with Evolution Strategies. Kluwer, 2002.

Digital Library

Google Scholar

[2]

D. V. Arnold and H.-G. Beyer. Local Performance of the (μ/μ_I, łambda)-ES in a Noisy Environment. In W. Martin and W. Spears, editors, Foundations of Genetic Algorithms, 6, pages 127--141. Morgan Kaufmann, 2001.

Crossref

Google Scholar

[3]

D. V. Arnold and A. MacLeod. Step length adaption on ridge functions. Evolutionary Computation, 16:151--184, 2008.

Digital Library

Google Scholar

[4]

H.-G. Beyer. The Theory of Evolution Strategies. Natural Computing Series, Springer, Heidelberg, 2001.

Digital Library

Google Scholar

[5]

H.-G. Beyer, M. Dobler, C. Hämmerle, and P. Masser. On Strategy Parameter Control by Meta-ES. GECCO-2009: Proceedings of the Genetic and Evolutionary Computation Conference, pages 499--506. ACM, 2009.

Digital Library

Google Scholar

[6]

H.-G. Beyer, M. Hellwig. Mutation Strength Control by Meta-ES on the Sharp Ridge. GECCO-2012: Proceedings of the Genetic and Evolutionary Computation Conference, pages 305--312. ACM, 2012.

Digital Library

Google Scholar

[7]

M. Herdy. Reproductive Isolation as Strategy Parameter in HierarchicallyOrganized Evolution Strategies. In R. Männer and B. Manderick, editors, Parallel ProblemSolving from Nature 2, pages 207--217. Elsevier, 1992.

Google Scholar

[8]

I. Rechenberg. Evolutionsstrategie '94. Frommann-Holzboog Verlag, Stuttgart, 1994.

Google Scholar

Cited By

View all

Duan QShao CZhou GZhang MZhao QShi Y(2024)Distributed Evolution Strategies With Multi-Level Learning for Large-Scale Black-Box OptimizationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.343768835:11(2087-2101)Online publication date: Nov-2024
https://doi.org/10.1109/TPDS.2024.3437688
Videau MLeite ATeytaud OSchoenauer M(2022)Multi-objective Genetic Programming for Explainable Reinforcement LearningGenetic Programming10.1007/978-3-031-02056-8_18(278-293)Online publication date: 13-Apr-2022
https://doi.org/10.1007/978-3-031-02056-8_18
Loshchilov ISchoenauer MSebag MHansen N(2014)Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ESParallel Problem Solving from Nature – PPSN XIII10.1007/978-3-319-10762-2_7(70-79)Online publication date: 2014
https://doi.org/10.1007/978-3-319-10762-2_7

Index Terms

Controlling population size and mutation strength by Meta-ES under fitness noise
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
      1. Computational control theory

Recommendations

On strategy parameter control by Meta-ES
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

This paper introduces simple control rules for the mutation strength and the parental population size using the Meta-ES approach. An in-depth analysis is presented on the mutation strength control using the sphere model. A heuristic formula for the ...
Population size reduction for the differential evolution algorithm

This paper studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually ...
Mutation strength control by meta-ES on the sharp ridge
GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

This paper investigates mutation strength control using Meta-ES on the sharp ridge. The asymptotical analysis presented allows for the prediction of the dynamics in ridge as well as in radial direction. Being based on this analysis the problem of the ...

Comments

Information & Contributors

Information

Published In

FOGA XII '13: Proceedings of the twelfth workshop on Foundations of genetic algorithms XII

January 2013

198 pages

ISBN:9781450319904

DOI:10.1145/2460239

General Chairs:
Frank Neumann
The University of Adelaide, Australia
,
Kenneth De Jong
George Mason University, USA

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 January 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

FOGA '13

Sponsor:

SIGEVO

FOGA '13: Foundations of Genetic Algorithms XII

January 16 - 20, 2013

Adelaide, Australia

Acceptance Rates

Overall Acceptance Rate 72 of 131 submissions, 55%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
62
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Duan QShao CZhou GZhang MZhao QShi Y(2024)Distributed Evolution Strategies With Multi-Level Learning for Large-Scale Black-Box OptimizationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.343768835:11(2087-2101)Online publication date: Nov-2024
https://doi.org/10.1109/TPDS.2024.3437688
Videau MLeite ATeytaud OSchoenauer M(2022)Multi-objective Genetic Programming for Explainable Reinforcement LearningGenetic Programming10.1007/978-3-031-02056-8_18(278-293)Online publication date: 13-Apr-2022
https://doi.org/10.1007/978-3-031-02056-8_18
Loshchilov ISchoenauer MSebag MHansen N(2014)Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ESParallel Problem Solving from Nature – PPSN XIII10.1007/978-3-319-10762-2_7(70-79)Online publication date: 2014
https://doi.org/10.1007/978-3-319-10762-2_7

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

On strategy parameter control by Meta-ES

Population size reduction for the differential evolution algorithm

Mutation strength control by meta-ES on the sharp ridge

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations