@inproceedings{aa217033019b4c028813911ca4cb1894,
title = "Adaptive Case Selection for Symbolic Regression in Grammatical Evolution",
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 {\textquoteleft}train/test split.{\textquoteright} 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.",
keywords = "Adaptive Selection, Computational Efficiency, Diversity, Grammatical Evolution, Symbolic Regression, Test Case Selection",
author = "Gupt, {Krishn Kumar} and Meghana Kshirsagar and Dias, {Douglas Mota} and Sullivan, {Joseph P.} and Conor Ryan",
note = "Publisher Copyright: {\textcopyright} 2023 by SCITEPRESS – Science and Technology Publications, Lda.; 15th International Joint Conference on Computational Intelligence, IJCCI 2023 ; Conference date: 13-11-2023 Through 15-11-2023",
year = "2023",
doi = "10.5220/0012175900003595",
language = "English",
series = "International Joint Conference on Computational Intelligence",
publisher = "Science and Technology Publications, Lda",
pages = "195--205",
editor = "{van Stein}, Niki and Francesco Marcelloni and Lam, {H. K.} and Marie Cottrell and Joaquim Filipe",
booktitle = "Proceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023",
address = "Portugal",
}