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Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics

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dc.contributor.authorDurrett, G.
dc.contributor.authorNeumann, F.
dc.contributor.authorO'Reilly, U.
dc.contributor.conferenceACM SIGEVO Workshop on Foundations of Genetic Algorithms (11th : 2011 : Schwarzenberg, Austria)
dc.date.issued2011
dc.description.abstractAnalyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this paper, we start the computational complexity analysis of genetic programming (GP). We set up several simplified GP algorithms and analyze them on two separable model problems, ORDER and MAJORITY, each of which captures a relevant facet of typical GP problems. Both analyses give first rigorous insights into aspects of GP design, highlighting in particular the impact of accepting or rejecting neutral moves and the importance of a local mutation operator.
dc.description.statementofresponsibilityGreg Durrett, Frank Neumann, Una-May O’Reilly
dc.description.urihttp://www.sigevo.org/foga-2011/
dc.identifier.citationProceeding: FOGA '11: Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, 2011: pp.69-80
dc.identifier.doi10.1145/1967654.1967661
dc.identifier.isbn9781450306331
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]
dc.identifier.urihttp://hdl.handle.net/2440/70655
dc.language.isoen
dc.publisherACM Press
dc.publisher.placeNew York
dc.rightsCopyright 2011 ACM
dc.source.urihttps://doi.org/10.1145/1967654.1967661
dc.subjectAlgorithms
dc.subjectTheory
dc.titleComputational complexity analysis of simple genetic programming on two problems modeling isolated program semantics
dc.typeConference paper
pubs.publication-statusPublished

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