Active learning improves performance on symbolic regression tasks in StackGP

Author:

Haut Nathan1,Banzhaf Wolfgang1,Punch Bill1

Affiliation:

1. Michigan State University

Funder

John R. Koza Endowment

Publisher

ACM

Reference8 articles.

1. Scaling Genetic Programming to Large Datasets Using Hierarchical Dynamic Subset Selection

2. Junio De Freitas , Gisele L Pappa , Altigran S da Silva , Marcos A Goncales , Edleno Moura , Adriano Veloso , Alberto HF Laender , and Moisés G de Carvalho . 2010 . Active Learning Genetic Programming for Record Deduplication. In IEEE Congress on Evolutionary Computation. 1--8. Junio De Freitas, Gisele L Pappa, Altigran S da Silva, Marcos A Goncales, Edleno Moura, Adriano Veloso, Alberto HF Laender, and Moisés G de Carvalho. 2010. Active Learning Genetic Programming for Record Deduplication. In IEEE Congress on Evolutionary Computation. 1--8.

3. Nathan Haut , Wolfgang Banzhaf , and Bill Punch . 2022. Active Learning Improves Performance on Symbolic Regression Tasks in StackGP. arXiv 2202.04708 ( 2022 ). Nathan Haut, Wolfgang Banzhaf, and Bill Punch. 2022. Active Learning Improves Performance on Symbolic Regression Tasks in StackGP. arXiv 2202.04708 (2022).

4. Mark Kotanchek and Nathan Haut . 2022. Back To The Future --- Revisiting OrdinalGP & Trustable Models After a Decade . In Genetic Programming Theory and Practice XVIII, Wolfgang Banzhaf, Leonardo Trujillo, Stephan Winkler, and Bill Worzel (Eds.). Springer , Singapore , 129--142. Mark Kotanchek and Nathan Haut. 2022. Back To The Future --- Revisiting OrdinalGP & Trustable Models After a Decade. In Genetic Programming Theory and Practice XVIII, Wolfgang Banzhaf, Leonardo Trujillo, Stephan Winkler, and Bill Worzel (Eds.). Springer, Singapore, 129--142.

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