Analyzing the impact of undersampling on the benchmarking and configuration of evolutionary algorithms

Author:

Vermetten Diederick1,Wang Hao1,López-Ibañez Manuel2,Doerr Carola3,Bäck Thomas1

Affiliation:

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

2. University of Málaga, Málaga, Spain

3. Sorbonne Université, Paris, France

Funder

Paris Ile-de-France region

Spanish Ministry of Science and Innovation (MICINN)

Publisher

ACM

Reference22 articles.

1. Thomas Bartz-Beielstein , Carola Doerr , Daan van den Berg , Jakob Bossek , Sowmya Chandrasekaran , Tome Eftimov , Andreas Fischbach , Pascal Kerschke , William La Cava , Manuel López-Ibáñez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, and Thomas Weise. 2020 . Benchmarking in Optimization : Best Practice and Open Issues. Arxiv preprint arXiv:2007.03488 [cs.NE] (2020). https://arxiv.org/abs/2007.03488 Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La Cava, Manuel López-Ibáñez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, and Thomas Weise. 2020. Benchmarking in Optimization: Best Practice and Open Issues. Arxiv preprint arXiv:2007.03488 [cs.NE] (2020). https://arxiv.org/abs/2007.03488

2. Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances

3. Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules

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