Semantic Cluster Operator for Symbolic Regression and Its Applications. (October 2022) - Jeong, Hoseong; Kim, Jae Hyun; Choi, Seung-Ho; Lee, Seokin; Heo, Inwook; Kim, Kang Su
HIGHLIGHTS: A novel symbolic regression algorithm for automatic derivation of empirical models. Rapid convergence performance by a novel searching strategy using semantic clusters. Enhancing generalization and bloat control performance by early stopping and pruning. High accuracy model comparable to the black box models despite its simplicity. ABSTRACT: In this paper, a novel operator, semantic cluster operator, was developed to overcome the low convergence performance of genetic programming in symbolic regression. The main strategy for steep convergence was to narrow search space and scrutinize the narrowed search space using a semantic cluster library. To demonstrate the success of this idea, the computation time and offspring fitness of the operator developed in this paper were compared with those of exhaustive search. The computation time of the operator was approximately 6% of that of the exhaustive search, and its offspring fitness was in the top 0.5% among all offspring derived from the exhaustive search. In two application problems, derived models from an algorithm using the operator showed high prediction accuracy comparable to an artificial neural network, random forest, and support vector machine despite its simplicity.