Adaptive Case Selection for Symbolic Regression in Grammatical Evolution

Krishn Kumar Gupt, Meghana Kshirsagar, Douglas Dias, Douglas Dias, Joseph Sullivan, Conor Ryan

2023

Abstract

The analysis of time efficiency and solution size has recently gained huge interest among researchers of Grammatical Evolution (GE). The voluminous data have led to slower learning of GE in finding innovative solutions to complex problems. Few works incorporate machine learning techniques to extract samples from big datasets. Most of the work in the field focuses on optimizing the GE hyperparameters. This leads to the motivation of our work, Adaptive Case Selection (ACS), a diversity-preserving test case selection method that adaptively selects test cases during the evolutionary process of GE. We used six symbolic regression synthetic datasets with diverse features and samples in the preliminary experimentation and trained the models using GE. Statistical Validation of results demonstrates ACS enhancing the efficiency of the evolutionary process. ACS achieved higher accuracy on all six problems when compared to conventional ‘train/test split.’ It outperforms four out of six problems against the recently proposed Distance-Based Selection (DBS) method while competitive on the remaining two. ACS accelerated the evolutionary process by a factor of 14X and 11X against both methods, respectively, and resulted in simpler solutions. These findings suggest ACS can potentially speed up the evolutionary process of GE when solving complex problems.

Paper Citation

in Harvard Style

Kumar Gupt K., Kshirsagar M., Dias D., Sullivan J. and Ryan C. (2023). Adaptive Case Selection for Symbolic Regression in Grammatical Evolution. In Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA; ISBN 978-989-758-674-3, SciTePress, pages 195-205. DOI: 10.5220/0012175900003595

in Bibtex Style

@conference{ecta23,
author={Krishn Kumar Gupt and Meghana Kshirsagar and Douglas Dias and Joseph Sullivan and Conor Ryan},
title={Adaptive Case Selection for Symbolic Regression in Grammatical Evolution},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA},
year={2023},
pages={195-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012175900003595},
isbn={978-989-758-674-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - Adaptive Case Selection for Symbolic Regression in Grammatical Evolution
SN - 978-989-758-674-3
AU - Kumar Gupt K.
AU - Kshirsagar M.
AU - Dias D.
AU - Sullivan J.
AU - Ryan C.
PY - 2023
SP - 195
EP - 205
DO - 10.5220/0012175900003595
PB - SciTePress