Where are we now?: a large benchmark study of recent symbolic regression methods

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

@InProceedings{Orzechowski:2018:GECCO,

• author = "Patryk Orzechowski and William {La Cava} and Jason H. Moore",
• title = "Where are we now?: a large benchmark study of recent symbolic regression methods",
• booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference",
• year = "2018",
• editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre",
• pages = "1183--1190",
• address = "Kyoto, Japan",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, SRBench, Ensemble methods, Cross-validation, symbolic regression, benchmarking, machine learning",
• isbn13 = "978-1-4503-5618-3",
• URL = "https://www.researchgate.net/publication/324769381_Where_are_we_now_A_large_benchmark_study_of_recent_symbolic_regression_methods",
• DOI = "doi:10.1145/3205455.3205539",
• code_url = "https://cavalab.org/srbench/",
• size = "8 pages",
• abstract = "we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use a set of nearly 100 regression benchmark problems culled from open source repositories across the web. We conduct a rigorous benchmarking of four recent symbolic regression approaches as well as nine machine learning approaches from scikit-learn. The results suggest that symbolic regression performs strongly compared to state-of-the-art gradient boosting algorithms, although in terms of running times is among the slowest of the available methodologies. We discuss the results in detail and point to future research directions that may allow symbolic regression to gain wider adoption in the machine learning community.",
• notes = "Also known as \cite{3205539} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)",

}

Genetic Programming entries for Patryk Orzechowski William La Cava Jason H Moore

Citations