Linear Combinations of Features As Leaf Nodes in Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Zegklitz:2017:GECCO,

• author = "Jan Zegklitz and Petr Posik",
• title = "Linear Combinations of Features As Leaf Nodes in Symbolic Regression",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4939-0",
• address = "Berlin, Germany",
• pages = "145--146",
• size = "2 pages",
• URL = "http://doi.acm.org/10.1145/3067695.3076009",
• DOI = "doi:10.1145/3067695.3076009",
• acmid = "3076009",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, symbolic regression",
• month = "15-19 " # jul,
• abstract = "We propose a new type of leaf node for use in Symbolic Regression (SR) that performs linear combinations of feature variables (LCF). LCF's weights are tuned using a gradient method based on back-propagation algorithm known from neural networks. Multi-Gene Genetic Programming (MGGP) was chosen as a baseline model. As a sanity check, we experimentally show that LCFs improve the performance of the baseline on a rotated toy SR problem. We then perform a thorough experimental study on a number of artificial and real-world SR benchmarks. The usage of LCFs in MGGP statically improved the results in 5 cases out of 9, while it worsen them in only a single case.",
• notes = "Also known as \cite{Zegklitz:2017:LCF:3067695.3076009} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Jan Zegklitz Petr Posik

Citations