Espresso to the rescue of Genetic Programming facing exponential complexity

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

@InProceedings{potvin:2022:GECCOcomp,

• author = "Nicolas Potvin and Hugues Bersini and Dragomir Milojevic",
• title = "Espresso to the rescue of Genetic Programming facing exponential complexity",
• 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 = "590--593",
• 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, fitness evaluation, optimisation, electronics",
• isbn13 = "978-1-4503-9268-6/22/07",
• DOI = "doi:10.1145/3520304.3529005",
• abstract = "Fitness computation is a crucial part of any population-based evolutionary strategy. In Genetic Programming (GP) each individual of a population is evaluated by comparing the output they return when fed some input with the expected output. The mapping input/output form the test cases, it describes the {"}behaviour{"} an individual should have when presented an instance of a problem. However, there exist situations in which the mapping is given as a function an individual should implement, thus consisting in combinations of several variables of the problem. This paper addresses the issue of efficiently computing the fitness of an individual evaluated on such test sets exponentially large in the number of variables. The inspiration came from digital electronics and more specifically the ESPRESSO-MV algorithm, accepting multi-valued variables. The proposed datastructure represents subsets of the solution space, hence allowing to compute the number of passed test cases as a single operation rather than enumerating all of them. The heuristics used rely on the Unate Recursive Paradigm (URP), some of the proposed algorithms are new while others come directly from ESPRESSO-MV.",
• 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 Nicolas Potvin Hugues Bersini Dragomir Milojevic

Citations