Constant versus variable arity operators in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{McMullin:2010:gecco,

• author = "Michael McMullin and Terence Soule",
• title = "Constant versus variable arity operators in genetic programming",
• booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation",
• year = "2010",
• editor = "Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and Carsten Witt",
• isbn13 = "978-1-4503-0072-8",
• pages = "987--988",
• keywords = "genetic algorithms, genetic programming, Poster",
• month = "7-11 " # jul,
• organisation = "SIGEVO",
• address = "Portland, Oregon, USA",
• DOI = "doi:10.1145/1830483.1830663",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "In this paper we compare typical variable arity operators to constant arity operators in which extraneous branches are treated as no-ops. We suggest that the consistent arity implementation would perform poorer than the variable arity implementation, due to a large amount of non-productive changes experienced during the early life of a consistent arity individual. Contrary to the expected result, both algorithms performed nearly identically. The consistent arity population developed a better average population faster, indicating that it would be a better option for tasks requiring many options for success. The variable arity population developed much smaller individuals on average, taking up much less space. This may be partly due to the large proportion of arity one operators in the operator set. However, a comparison of the execution times produced surprisingly mixed results, with the variable arity approach sometime taking significantly more time despite producing significantly smaller trees.",
• notes = "truck backing up, symbolic regression, inter-twined spirals, Also known as \cite{1830663} GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)",

}

Genetic Programming entries for Michael McMullin Terence Soule

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