Interaction-Transformation Evolutionary Algorithm with coefficients optimization

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

@InProceedings{aldeia:2022:SymReg,

• author = "Guilherme Seidyo Imai Aldeia and Fabricio {Olivetti de Franca}",
• title = "{Interaction-Transformation} Evolutionary Algorithm with coefficients optimization",
• booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion",
• year = "2022",
• editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson",
• pages = "2274--2281",
• address = "Boston, USA",
• series = "GECCO '22",
• month = "9-13 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, Representation of mathematical functions, symbolic regression, coefficient optimization, benchmark, evolutionary algorithm",
• isbn13 = "978-1-4503-9268-6/22/07",
• DOI = "doi:10.1145/3520304.3533987",
• size = "8 pages",
• abstract = "Symbolic Regression is the task of finding a mathematical expression to describe the relationship between one or more independent variables with a dependent variable. The search space can be vast and include any algebraic function; thus, finding optimal values for coefficients may not be a trivial task. The Interaction-Transformation representation alleviates this problem enforcing that the coefficients of the expression is part of a linear transformation, allowing the application of least squares. But this solution also limits the search space of the expressions. This paper proposes four different strategies to optimize the coefficients of the nonlinear part of the Interaction-Transformation representation. We benchmark the proposed strategies by applying the Interaction-Transformation Evolutionary Algorithm (ITEA) to six well-known data sets to evaluate four optimization heuristics combining linear and non-linear methods. The results show that optimizing the non-linear and linear coefficients separately was the best strategy to find better-performing expressions with a higher run-time and expression size. The non-linear optimization method alone was the worst-performing method.",
• notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Guilherme Seidyo Imai Aldeia Fabricio Olivetti de Franca

Citations