Authors:
Krishn Kumar Gupt
1
;
Meghana Kshirsagar
2
;
Douglas Dias
2
;
3
;
Joseph Sullivan
1
and
Conor Ryan
2
Affiliations:
1
Technological University of the Shannon: Midlands Midwest, Moylish campus, Limerick, Ireland
;
2
Biocomputing and Development System Lab, University of Limerick, Ireland
;
3
Department of Electronics & Telecommunications, Rio de Janeiro State University, Rio de Janeiro, Brazil
Keyword(s):
Test Case Selection, Adaptive Selection, Symbolic Regression, Grammatical Evolution, Diversity, Computational Efficiency.
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.
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