Improved Genetic Programming for Symbolic Regression: Case Studies on Practical Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Huynh:2022:SSCI,

• author = "Quang Huynh and Hemant Singh and Tapabrata Ray and Akira Oyama",
• booktitle = "2022 IEEE Symposium Series on Computational Intelligence (SSCI)",
• title = "Improved Genetic Programming for Symbolic Regression: Case Studies on Practical Applications",
• year = "2022",
• pages = "1135--1142",
• abstract = "Genetic Programming (GP), especially Semantic GP (SGP), has shown significant potential in solving numerical benchmarks in Symbolic Regression (SR) domain in recent years. However, its application on real-world problems has been less explored due to the large sizes of the resulting expressions, which are prone to over-fitting and are difficult to interpret. In this paper, we propose a method that incorporates customization for real-world data sets based on a combination of two operators of GP. The first operator uses the concept of Semantic Backpropagation, a noteworthy method in SGP, to create short expressions which are highly correlated with the outputs. The second operator makes use of Mixed Integer Linear Programming (MILP) to combine short expressions into the overall expression with good accuracy. The proposed approach is tested on one synthetic data set and two practical applications which are challenging for conventional GP. The experimental results are very promising, with further scope of improvement.",
• keywords = "genetic algorithms, genetic programming, Backpropagation, Sensitivity analysis, Semantics, Benchmark testing, Mixed integer linear programming, Computational intelligence",
• DOI = "doi:10.1109/SSCI51031.2022.10022279",
• month = dec,
• notes = "Also known as \cite{10022279}",

}

Genetic Programming entries for Quang Nhat Huynh Hemant Singh Tapabrata Ray Akira Oyama

Citations