Semantic Cluster Operator for Symbolic Regression and Its Applications

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@Article{JEONG:2022:advengsoft,

• author = "Hoseong Jeong and Jae Hyun Kim and Seung-Ho Choi and Seokin Lee and Inwook Heo and Kang Su Kim",
• title = "Semantic Cluster Operator for Symbolic Regression and Its Applications",
• journal = "Advances in Engineering Software",
• volume = "172",
• pages = "103174",
• year = "2022",
• ISSN = "0965-9978",
• DOI = "doi:10.1016/j.advengsoft.2022.103174",
• URL = "https://www.sciencedirect.com/science/article/pii/S0965997822000850",
• keywords = "genetic algorithms, genetic programming, Automatic code derivation, Semantic, Clustering, Iterated local search, Symbolic regression",
• abstract = "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 6percent of that of the exhaustive search, and its offspring fitness was in the top 0.5percent 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.",

}

Genetic Programming entries for Hoseong Jeong Jae-Hyun Kim Seung-Ho Choi Seokin Lee Inwook Heo Kang Su Kim

Citations