Investigation of Behavioral Cloning Guided Genetic Programming Using a Multilayer Perceptron for Symbolic Regression
Journal
Communications in Computer and Information Science
Journal Volume
2828 CCIS
Start Page
295
End Page
310
ISSN
1865-0929
1865-0937
ISBN (of the container)
978-303215634-1
ISBN
9783032156341
9783032156358
Date Issued
2026-02-01
Author(s)
Lee, Liang-Wei
Abstract
This paper proposes a genetic programming (GP) algorithm for symbolic regression (SR), called the behavioral cloning guided genetic programming (BCGP) algorithm. The goal is to improve the effectiveness of crossover by preserving the relationship between parent operators and subtrees through imitating the behavior of subtree crossover during evolution. Specifically, BCGP investigates the application of a multilayer perceptron to capture features based on the parent operators and the subtrees. Across the benchmark problems from the SR benchmark and the Feyman SR database, BCGP gains the lowest count of maximum average MAEs and lowest average ranks, and statistically significantly outperforms ellynGP, and GP-GOMEA on 31 and 9 benchmark problems, respectively, out of a total of 43.
Event(s)
17th International Joint Conference on Computational Intelligence, IJCCI 2025
Subjects
Genetic Programming
Multilayer Perceptron
Symbolic Regression
Publisher
Springer Nature Switzerland
Type
conference paper