Feature Engineering for Improving Robustness of Crossover in Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{Sambo:2020:GECCOcomp,

• author = "Aliyu Sani Sambo and R. Muhammad Atif Azad and Yevgeniya Kovalchuk and Vivek Padmanaabhan Indramohan and Hanifa Shah",
• title = "Feature Engineering for Improving Robustness of Crossover in Symbolic Regression",
• year = "2020",
• editor = "Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William {La Cava} and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe {De Falco} and Antonio {Della Cioppa} and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik",
• isbn13 = "9781450371278",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• URL = "https://doi.org/10.1145/3377929.3390078",
• DOI = "10.1145/3377929.3390078",
• booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",
• pages = "249--250",
• size = "2 pages",
• keywords = "genetic algorithms, genetic programming, feature engineering, regression",
• address = "internet",
• series = "GECCO '20",
• month = jul # " 8-12",
• organisation = "SIGEVO",
• abstract = "Isolating the fitness-contribution of substructures is typically a difficult task in Genetic Programming (GP). Hence, useful substructures are lost when the overall structure (model) performs poorly. Furthermore, while crossover is heavily used in GP, it typically produces offspring models with significantly lower fitness than that of the parents. In symbolic regression, this degradation also occurs because the coefficients of an evolving model lose utility after crossover. This paper proposes isolating the fitness-contribution of various substructures and reducing the negative impact of crossover by evolving a set of features instead of monolithic models. The method then leverages multiple linear regression (MLR) to optimise the coefficients of these features. Since adding new features cannot degrade the accuracy of an MLR produced model, MLR-aided GP models can bloat. To penalise such additions, we use Adjusted R2 as the fitness function. The paper compares the proposed method with standard GP and GP with linear scaling. Experimental results show that the proposed method matches the accuracy of the competing methods within only 1/10th of the number of generations. Also, the method significantly decreases the rate of post-crossover fitness degradation.",
• notes = "Also known as \cite{10.1145/3377929.3390078} 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 Aliyu Sani Sambo R Muhammad Atif Azad Yevgeniya Kovalchuk Vivek Padmanaabhan Indramohan Hanifa Shah

Citations