Adaptive Case Selection for Symbolic Regression in Grammatical Evolution

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023
EditorsNiki van Stein, Francesco Marcelloni, H. K. Lam, Marie Cottrell, Joaquim Filipe
PublisherScience and Technology Publications, Lda
Pages195-205
Number of pages11
ISBN (Electronic)9789897586743
DOIs
Publication statusPublished - 2023
Event15th International Joint Conference on Computational Intelligence, IJCCI 2023 - Hybrid, Rome, Italy
Duration: 13 Nov 202315 Nov 2023

Publication series

NameInternational Joint Conference on Computational Intelligence
ISSN (Electronic)2184-3236

Conference

Conference15th International Joint Conference on Computational Intelligence, IJCCI 2023
Country/TerritoryItaly
CityHybrid, Rome
Period13/11/2315/11/23

Keywords

  • Adaptive Selection
  • Computational Efficiency
  • Diversity
  • Grammatical Evolution
  • Symbolic Regression
  • Test Case Selection

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