Taylor Polynomial Enhancer Using Genetic Programming for Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7325

@InProceedings{chang:2023:GECCOcomp,

• author = "Chi-Hsien Chang and Tu-Chin Chiang and Tzu-Hao Hsu and Ting-Shuo Chuang and Wen-Zhong Fang and Tian-Li Yu",
• title = "Taylor Polynomial Enhancer Using Genetic Programming for Symbolic Regression",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "543--546",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, symbolic regression, taylor polynomial: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590591",
• size = "4 pages",
• abstract = "Unlike most research of symbolic regression with genetic programming (GP) concerning black-box optimization, this paper focuses on the scenario where the underlying function is available, but due to limited computational resources or product imperfection, the function needs to be approximated with simplicity to fit measured data. Taylor polynomial (TP) is commonly used in such scenario; however, its performance drops drastically away from the expansion point. On the other hand, solely using GP does not utilize the knowledge of the underlying function, even though possibly inaccurate. This paper proposes using GP as a TP enhancer, namely TPE-GP, to combine the advantages from TP and GP. Specifically, TPE-GP utilizes infinite-order operators to compensate the power of TP with finite order. Empirically, on functions that are expressible by TP, TP outperformed both gplearn and TPE-GP as expected, while TPE-GP outperformed gplearn due to the use of TP. On functions that are not expressible by TP but expressible by the function set (FS), TPE-GP was competitive with gplearn while both outperformed TP. Finally, on functions that are not expressible by both TP and FS, TPE-GP outperformed both TP and gplearn, indicating the hybrid did achieve the synergy effect from TP and GP.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Chi-Hsien Chang Tu-Chin Chiang Tzu-Hao Hsu Ting-Shuo Chuang Wen-Zhong Fang Tian-Li Yu

Citations