MetaSR: A Meta-Learning Approach to Fitness Formulation for Frequency-Aware Symbolic Regression-Reference-Cited by-同舟云学术

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MetaSR: A Meta-Learning Approach to Fitness Formulation for Frequency-Aware Symbolic Regression

Published:2024-07-14 Issue: Volume:530 Page:878-886
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
language:
Short-container-title:

Author:

Fong Kei Sen¹^ORCID,Motani Mehul¹^ORCID

Affiliation:

1. National University of Singapore, Singapore, Singapore

Funder

National Research Foundation, Singapore under its AI Singapore Programme

A*STAR, CISCO Systems (USA) Pte. Ltd and National University of Singapore under its Cisco-NUS Accelerated Digital Economy Corporate Laboratory

Publisher

ACM

Reference26 articles.

1. Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear Scaling

2. Symbolic Regression Trees as Embedded Representations

3. Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems

4. Reducing Overparameterization of Symbolic Regression Models with Equality Saturation

5. Kei Sen Fong, Shelvia Wongso, and Mehul Motani. Rethinking symbolic regression: Morphology and adaptability in the context of evolutionary algorithms. In International Conference on Learning Representations, 2023.

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