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

Authors:

Kouhei Nishida,

Youhei AkimotoAuthors Info & Claims

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

Pages 1193 - 1200

https://doi.org/10.1145/2908961.2931701

Published: 20 July 2016 Publication History

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Abstract

The CMA-ES with a population size adaptation mechanism is benchmarked on the BBOB noisy testbed. The population size is adapted online based on the signal-to-noise ratio of the update of the distribution parameters such as the mean vector and the covariance matrix. Four variants of the population adaptation mechanism with a random restart strategy and the BIPOP-CMA-ES are compared.

References

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K. Nishida and Y. Akimoto. Evaluating the population size adaptation mechanism for CMA-ES on the BBOB noiseless testbed. In Workshop Proceedings of Genetic and Evolutionary Computation Conference, ACM, 2016. To appear.

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Cited By

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Nguyen D(2024)Adapting the population size in CMA-ES using nearest-better clustering method for multimodal optimizationApplied Soft Computing10.1016/j.asoc.2024.112361167(112361)Online publication date: Dec-2024
https://doi.org/10.1016/j.asoc.2024.112361
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

Index Terms

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

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Published In

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

New York, NY, United States

Publication History

Published: 20 July 2016

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Funding Sources

Japan Society for the Promotion of Science

Conference

GECCO '16

Sponsor:

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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Cited By

View all

Nguyen D(2024)Adapting the population size in CMA-ES using nearest-better clustering method for multimodal optimizationApplied Soft Computing10.1016/j.asoc.2024.112361167(112361)Online publication date: Dec-2024
https://doi.org/10.1016/j.asoc.2024.112361
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

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

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

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