research-article

Benchmarking CMA-ES with margin on the bbob-mixint testbed

Authors:

Masahiro Nomura,

Shinichi ShirakawaAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1708 - 1716

https://doi.org/10.1145/3520304.3534043

Published: 19 July 2022 Publication History

Abstract

The CMA-ES with Margin (CMA-ESwM) is a CMA-ES variant recently proposed for mixed-integer black-box optimization (MI-BBO), which introduces a lower bound on the marginal probability associated with integer variables. The CMA-ESwM shows promising performance compared to existing methods on simple benchmark functions. However, its performance has not been comprehensively investigated in other function classes, such as multimodal ones. In this work, we investigate the performance of the CMA-ESwM on the bbob-mixint testbed that includes problems of various properties for MI-BBO. The experimental results show that the CMA-ESwM outperforms the other MI-BBO methods at higher dimensions. The performance at low dimensions is competitive with the comparative methods.

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Ryoki Hamano, Shota Saito, Masahiro Nomura, and Shinichi Shirakawa. 2022. CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '22). ACM. To appear.

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Cited By

Tanabe RLi XHandl J(2024)Benchmarking Parameter Control Methods in Differential Evolution for Mixed-Integer Black-Box OptimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654019(712-721)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654019
Marty THansen NAuger ASemet YHéron S(2024)LB+IC-CMA-ES: Two Simple Modifications of CMA-ES to Handle Mixed-Integer ProblemsParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70068-2_18(284-299)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70068-2_18
Marty TSemet YAuger AHéron SHansen NSilva SPaquete L(2023)Benchmarking CMA-ES with Basic Integer Handling on a Mixed-Integer Test Problem SuiteProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596411(1628-1635)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596411
Show More Cited By

Index Terms

Benchmarking CMA-ES with margin on the bbob-mixint testbed
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Continuous space search

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cover image ACM Conferences

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Copyright © 2022 ACM.

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Publication History

Published: 19 July 2022

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Japan Society for the Promotion of Science

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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Cited By

Tanabe RLi XHandl J(2024)Benchmarking Parameter Control Methods in Differential Evolution for Mixed-Integer Black-Box OptimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654019(712-721)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654019
Marty THansen NAuger ASemet YHéron S(2024)LB+IC-CMA-ES: Two Simple Modifications of CMA-ES to Handle Mixed-Integer ProblemsParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70068-2_18(284-299)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70068-2_18
Marty TSemet YAuger AHéron SHansen NSilva SPaquete L(2023)Benchmarking CMA-ES with Basic Integer Handling on a Mixed-Integer Test Problem SuiteProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596411(1628-1635)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596411
Thomaser ADe Nobel JVermetten DYe FBäck TKononova ASilva SPaquete L(2023)When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous ProblemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590410(856-863)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590410

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