Small Solutions for Real-World Symbolic Regression using Denoising Autoencoder Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Wittenberg:2023:EuroGP,

• author = "David Wittenberg and Franz Rothlauf",
• title = "Small Solutions for Real-World Symbolic Regression using Denoising Autoencoder Genetic Programming",
• booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming",
• year = "2023",
• month = "12-14 " # apr,
• editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
• series = "LNCS",
• volume = "13986",
• publisher = "Springer Verlag",
• address = "Brno, Czech Republic",
• pages = "101--116",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Estimation of Distribution Algorithms, EDA, Denoising Autoencoders, Symbolic Regression",
• isbn13 = "978-3-031-29572-0",
• URL = "https://rdcu.be/c8UQO",
• DOI = "doi:10.1007/978-3-031-29573-7_7",
• size = "16 pages",
• abstract = "Denoising Autoencoder Genetic Programming (DAE-GP) is a model-based evolutionary algorithm that uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard recombination and mutation operators of genetic programming (GP). In this paper, we use the DAE-GP to solve a set of nine standard real-world symbolic regression tasks. We compare the prediction quality of the DAE-GP to standard GP, geometric semantic GP (GSGP), and the gene-pool optimal mixing evolutionary algorithm for GP (GOMEA-GP), and find that the DAE-GP shows similar prediction quality using a much lower number of fitness evaluations than GSGP or GOMEA-GP. In addition, the DAE-GP consistently finds small solutions. The best candidate solutions of the DAE-GP are 69percent smaller (median number of nodes) than the best candidate solutions found by standard GP. An analysis of the bias of the selection and variation step for both the DAE-GP and standard GP gives insight into why differences in solution size exist: the strong increase in solution size for standard GP is a result of both selection and variation bias. The results highlight that learning and sampling from a probabilistic model is a promising alternative to classic GP variation operators where the DAE-GP is able to generate small solutions for real-world symbolic regression tasks.",
• notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023",

}

Genetic Programming entries for David Wittenberg Franz Rothlauf

Citations