research-article

Benchmarking the novel CMA-ES restart strategy using the search history on the BBOB noiseless testbed

Authors:

Takahiro Yamaguchi,

Youhei AkimotoAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1780 - 1787

https://doi.org/10.1145/3067695.3084203

Published: 15 July 2017 Publication History

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Abstract

In this paper we propose a termination mechanism and the initial step-size control mechanism for restart strategies in the CMA-ES. The proposed mechanism utilizes a history of the distribution parameters from past restarts to early terminate an overlapping exploitation of the search domain. The initial step-size is controlled so that the next restart will not overlap with past restarts. The proposed mechanism is combined with a simple restart, IPOP restart and BIPOP restart strategies. The effectiveness and the drawback of the proposed mechanism is demonstrated on the BBOB noiseless testbed.

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Cited By

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Zaborski MMańdziuk J(2023)LQ-R-SHADE: R-SHADE with Quadratic Surrogate ModelArtificial Intelligence and Soft Computing10.1007/978-3-031-23492-7_23(265-276)Online publication date: 24-Jan-2023
https://doi.org/10.1007/978-3-031-23492-7_23
Zaborski MMańdziuk JWagner M(2022)Improving LSHADE by means of a pre-screening mechanismProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528805(884-892)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528805
Zaborski MMańdziuk J(2022)Surrogate-Assisted LSHADE Algorithm Utilizing Recursive Least Squares FilterParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14714-2_11(146-159)Online publication date: 14-Aug-2022
https://doi.org/10.1007/978-3-031-14714-2_11
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Index Terms

Benchmarking the novel CMA-ES restart strategy using the search history on the BBOB noiseless testbed
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization
        Bio-inspired optimization

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GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

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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Association for Computing Machinery

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Publication History

Published: 15 July 2017

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Zaborski MMańdziuk J(2023)LQ-R-SHADE: R-SHADE with Quadratic Surrogate ModelArtificial Intelligence and Soft Computing10.1007/978-3-031-23492-7_23(265-276)Online publication date: 24-Jan-2023
https://doi.org/10.1007/978-3-031-23492-7_23
Zaborski MMańdziuk JWagner M(2022)Improving LSHADE by means of a pre-screening mechanismProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528805(884-892)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528805
Zaborski MMańdziuk J(2022)Surrogate-Assisted LSHADE Algorithm Utilizing Recursive Least Squares FilterParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14714-2_11(146-159)Online publication date: 14-Aug-2022
https://doi.org/10.1007/978-3-031-14714-2_11
Zaborski MOkulewicz MMańdziuk J(2020)Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization frameworkFoundations of Computing and Decision Sciences10.2478/fcds-2020-001345:3(233-254)Online publication date: 18-Sep-2020
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Steinhoff VKerschke PAspar PTrautmann HGrimme C(2020)Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent2020 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI47803.2020.9308259(2445-2452)Online publication date: 1-Dec-2020
https://doi.org/10.1109/SSCI47803.2020.9308259
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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Benchmarking the PSA-CMA-ES on the BBOB noiseless testbed

Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed

Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed

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