Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing-Reference-Cited by-同舟云学术

Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing

Published:2024-07-14 Issue: Volume:1 Page:896-904
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Imai Aldeia Guilherme Seidyo¹^ORCID,De França Fabrício Olivetti¹^ORCID,La Cava William G.²³^ORCID

Affiliation:

1. Federal University of ABC, Santo Andre, SP, Brazil

2. Harvard, Boston, MA, United States of America

3. Boston Children's Hospital, Boston, MA, United States of America

Funder

NIH (National Institutes of Health)

Patient-Centered Outcomes Research Institute

FAPESP

CNPq

CAPES

Publisher

ACM

Reference29 articles.

1. Michael Affenzeller, Stephan M Winkler, Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu, and Stefan Wagner. 2014. Gaining deeper insights in symbolic regression. Genetic Programming Theory and Practice XI (2014), 175--190.

2. Interaction-transformation evolutionary algorithm with coefficients optimization

3. Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

4. Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regression

5. Bogdan Burlacu Lukas Kammerer Michael Affenzeller and Gabriel Kronberger. 2021. Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression. arXiv:2107.10640 [cs]. 10.1007/2F978-3-030-45093-9_44

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