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Evaluating the Population Size Adaptation Mechanism for CMA-ES on the BBOB Noiseless Testbed

Authors:

Kouhei Nishida,

Youhei AkimotoAuthors Info & Claims

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

Pages 1185 - 1192

https://doi.org/10.1145/2908961.2931700

Published: 20 July 2016 Publication History

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Abstract

The population size adaptation mechanism for CMA-ES is evaluated on the BBOB noiseless testbed. The population size is adapted on the basis of the estimated accuracy of the update of the distribution parameters, i.e., the mean vector and the covariance matrix of the Gaussian distribution. The population size is adapted so that the estimated accuracy of the parameter update keeps a certain level. The CMA- ES with the population size adaptation mechanism could solve well-structured multimodal functions as efficiently as the best 2009 portfolio without a restart strategy that in- creases the population size every restart such as the IPOP strategy.

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Parashar NRastogi DJohri PKhatri SYadav SJohri M(2024)Dynamic motion based evolutionary algorithm for enhancement of the search capability for global search spaceInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02556-915:12(5653-5675)Online publication date: 21-Oct-2024
https://doi.org/10.1007/s13198-024-02556-9
Molina DLaTorre AHerrera F(2018)An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of CompetitionsCognitive Computation10.1007/s12559-018-9554-010:4(517-544)Online publication date: 27-Apr-2018
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Index Terms

Evaluating the Population Size Adaptation Mechanism for CMA-ES on the BBOB Noiseless Testbed
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization
        Nonconvex optimization

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Estimation-of-distribution algorithm using Cauchy sampling distribution is compared with the bi-population CMA evolutionary strategy which was one of the best contenders in the black-box optimization benchmarking workshop in 2009. The results clearly ...

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GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

July 2016

1510 pages

ISBN:9781450343237

DOI:10.1145/2908961

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

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 the author(s) 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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Association for Computing Machinery

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Published: 20 July 2016

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Kayamori Foundation of Informational Science Advancement
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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Companion Paper Acceptance Rate 137 of 381 submissions, 36%;

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

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Yang ZDeng LLi CQin YZhang L(2025)A domain-transformed surrogate-assisted differential evolutionary algorithm for hyperparameter optimisation of satellite handover strategyInformation Sciences10.1016/j.ins.2024.121835700(121835)Online publication date: May-2025
https://doi.org/10.1016/j.ins.2024.121835
Parashar NRastogi DJohri PKhatri SYadav SJohri M(2024)Dynamic motion based evolutionary algorithm for enhancement of the search capability for global search spaceInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02556-915:12(5653-5675)Online publication date: 21-Oct-2024
https://doi.org/10.1007/s13198-024-02556-9
Molina DLaTorre AHerrera F(2018)An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of CompetitionsCognitive Computation10.1007/s12559-018-9554-010:4(517-544)Online publication date: 27-Apr-2018
https://doi.org/10.1007/s12559-018-9554-0
Nishida KAkimoto YNeumann FSutton A(2016)Evaluating the Population Size Adaptation Mechanism for CMA-ES on the BBOB Noisy TestbedProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2931701(1193-1200)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2931701

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Evaluating the Population Size Adaptation Mechanism for CMA-ES on the BBOB Noisy Testbed

Benchmarking the PSA-CMA-ES on the BBOB noiseless testbed

Comparison of cauchy EDA and BIPOP-CMA-ES algorithms on the BBOB noiseless testbed

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