The importance of landscape features for performance prediction of modular CMA-ES variants-Reference-Cited by-同舟云学术

The importance of landscape features for performance prediction of modular CMA-ES variants

Published:2022-07-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Kostovska Ana¹^ORCID,Vermetten Diederick²^ORCID,Džeroski Sašo³^ORCID,Doerr Carola⁴^ORCID,Korosec Peter³^ORCID,Eftimov Tome³^ORCID

Affiliation:

1. Jožef Stefan Institute &, Ljubljana, Slovenia

2. Leiden Institute for Advanced Computer Science, Leiden, The Netherlands

3. Jožef Stefan Institute, Ljubljana, Slovenia

4. Sorbonne Université, Paris, France

Funder

Slovenian Research Agency

Paris Ile-de-France region

European Commission

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3512290.3528832

Reference37 articles.

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3. Towards large scale automated algorithm design by integrating modular benchmarking frameworks

4. Thomas Bartz-Beielstein , Carola Doerr , Daan van den Berg , Jakob Bossek , Sowmya Chandrasekaran , Tome Eftimov , Andreas Fischbach , Pascal Kerschke , William La Cava , Manuel Lopez-Ibanez, et al. 2020 . Benchmarking in optimization: Best practice and open issues. arXiv preprint arXiv:2007.03488 (2020). Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La Cava, Manuel Lopez-Ibanez, et al. 2020. Benchmarking in optimization: Best practice and open issues. arXiv preprint arXiv:2007.03488 (2020).

5. Per instance algorithm configuration of CMA-ES with limited budget

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