A comparative study of GP-based and state-of-the-art classifiers on a synthetic machine learning benchmark-Reference-Cited by-同舟云学术

A comparative study of GP-based and state-of-the-art classifiers on a synthetic machine learning benchmark

Published:2022-07-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
language:
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Author:

Orzechowski Patryk¹,Renc Paweł²,La Cava William³,Moore Jason H.⁴,Sitek Arkadiusz²,Wąs Jaroslaw⁵,Wagenaar Joost¹

Affiliation:

1. University of Pennsylvania

2. Sano, Kraków, Poland

3. Harvard Medical School

4. Cedars-Sinai Medical Center

5. AGH University of Science and Technology, Krakow, Poland

Funder

Horizon 2020 Framework Programme

National Institutes of Health

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3520304.3529056

Reference27 articles.

1. Takuya Akiba , Shotaro Sano , Toshihiko Yanase , Takeru Ohta , and Masanori Koyama . 2019 . Optuna: A Next-generation Hyperparameter Optimization Framework. arXiv:1907.10902 [cs.LG] Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. arXiv:1907.10902 [cs.LG]

2. Bernd Bischl , Giuseppe Casalicchio , Matthias Feurer , Frank Hutter , Michel Lang , Rafael G Mantovani , Jan N van Rijn , and Joaquin Vanschoren . 2017. OpenML benchmarking suites and the OpenML100. stat 1050 ( 2017 ), 11. Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael G Mantovani, Jan N van Rijn, and Joaquin Vanschoren. 2017. OpenML benchmarking suites and the OpenML100. stat 1050 (2017), 11.

3. An Empirical Study of Progressive Insular Cooperative GP

4. A Survey on the Application of Genetic Programming to Classification

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