Feature Standardisation and Coefficient Optimisation for Effective Symbolic Regression

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

@InProceedings{Dick:2020:GECCO,

• author = "Grant Dick and Caitlin A. Owen and Peter A. Whigham",
• title = "Feature Standardisation and Coefficient Optimisation for Effective Symbolic Regression",
• year = "2020",
• editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann",
• isbn13 = "9781450371285",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• URL = "https://doi.org/10.1145/3377930.3390237",
• DOI = "doi:10.1145/3377930.3390237",
• booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference",
• pages = "306--314",
• size = "9 pages",
• keywords = "genetic algorithms, genetic programming, gradient descent, symbolic regression, feature standardisation",
• address = "internet",
• series = "GECCO '20",
• month = jul # " 8-12",
• organisation = "SIGEVO",
• abstract = "Symbolic regression is a common application of genetic programming where model structure and corresponding parameters are evolved in unison. In the majority of work exploring symbolic regression, features are used directly without acknowledgement of their relative scale or unit. This paper extends recent work on the importance of standardisation of features when conducting symbolic regression. Specifically, z-score standardisation of input features is applied to both inputs and response to ensure that evolution explores a model space with zero mean and unit variance. This paper demonstrates that standardisation allows a simpler function set to be used without increasing bias. Additionally, it is demonstrated that standardisation can significantly improve the performance of coefficient optimisation through gradient descent to produce accurate models. Through analysis of several benchmark data sets, we demonstrate that feature standardisation enables simple but effective approaches that are comparable in performance to the state-of-the-art in symbolic regression.",
• notes = "Also known as \cite{10.1145/3377930.3390237} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Grant Dick Caitlin A Owen Peter Alexander Whigham

Citations