Active Learning Informs Symbolic Regression Model Development in Genetic Programming

Author:

Haut Nathan1ORCID,Punch Bill1ORCID,Banzhaf Wolfgang1ORCID

Affiliation:

1. Michigan State University, East Lansing, United States of America

Publisher

ACM

Reference6 articles.

1. Nathan Haut . [n. d.]. StackGP. Retrieved February 2, 2023 from https://github.com/hoolagans/StackGP Nathan Haut. [n. d.]. StackGP. Retrieved February 2, 2023 from https://github.com/hoolagans/StackGP

2. Active learning improves performance on symbolic regression tasks in StackGP

3. Mark Kotanchek , Guido Smits , and Ekaterina Vladislavleva . 2007. Pursuing The Pareto Paradigm: Tournaments , Algorithm Variations , And Ordinal Optimization. In Genetic Programming Theory and Practice IV, Rick Riolo, Terence Soule, and Bill Worzel (Eds.). Springer , 167--185. Mark Kotanchek, Guido Smits, and Ekaterina Vladislavleva. 2007. Pursuing The Pareto Paradigm: Tournaments, Algorithm Variations, And Ordinal Optimization. In Genetic Programming Theory and Practice IV, Rick Riolo, Terence Soule, and Bill Worzel (Eds.). Springer, 167--185.

4. Max Tegmark . [n. d.]. Welcome to the Feynman Symbolic Regression Database! Retrieved January 26, 2022 from https://space.mit.edu/home/tegmark/aifeynman.html#:~:text=As%20opposed%20to%20linear%20regression,any%20combination%20of%20mathematical%20symbols. Max Tegmark. [n. d.]. Welcome to the Feynman Symbolic Regression Database! Retrieved January 26, 2022 from https://space.mit.edu/home/tegmark/aifeynman.html#:~:text=As%20opposed%20to%20linear%20regression,any%20combination%20of%20mathematical%20symbols.

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