Towards Automatic Grammatical Evolution for Real-world Symbolic Regression

Muhammad Ali, Meghana Kshirsagar, Enrique Naredo, Conor Ryan

2021

Abstract

AutoGE (Automatic Grammatical Evolution) is a tool designed to aid users of GE for the automatic estimation of Grammatical Evolution (GE) parameters, a key one being the grammar. The tool comprises of a rich suite of algorithms to assist in fine tuning a BNF (Backus-Naur Form) grammar to make it adaptable across a wide range of problems. It primarily facilitates the identification of better grammar structures and the choice of function sets to enhance existing fitness scores at a lower computational overhead. This research work discusses and reports experimental results for our Production Rule Pruning algorithm from AutoGE which employs a simple frequency-based approach for eliminating less useful productions. It captures the relationship between production rules and function sets involved in the problem domain to identify better grammar. The experimental study incorporates an extended function set and common grammar structures for grammar definition. Preliminary results based on ten popular real-world regression datasets demonstrate that the proposed algorithm not only identifies suitable grammar structures, but also prunes the grammar which results in shorter genome length for every problem, thus optimizing memory usage. Despite utilizing a fraction of budget in pruning, AutoGE was able to significantly enhance test scores for 3 problems.

Paper Citation

in Harvard Style

Ali M., Kshirsagar M., Naredo E. and Ryan C. (2021). Towards Automatic Grammatical Evolution for Real-world Symbolic Regression. In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: IJCCI; ISBN 978-989-758-534-0, SciTePress, pages 68-78. DOI: 10.5220/0010691500003063

in Bibtex Style

@conference{ijcci21,
author={Muhammad Ali and Meghana Kshirsagar and Enrique Naredo and Conor Ryan},
title={Towards Automatic Grammatical Evolution for Real-world Symbolic Regression},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: IJCCI},
year={2021},
pages={68-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010691500003063},
isbn={978-989-758-534-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: IJCCI
TI - Towards Automatic Grammatical Evolution for Real-world Symbolic Regression
SN - 978-989-758-534-0
AU - Ali M.
AU - Kshirsagar M.
AU - Naredo E.
AU - Ryan C.
PY - 2021
SP - 68
EP - 78
DO - 10.5220/0010691500003063
PB - SciTePress