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dc.contributor.authorKotzing, T.-
dc.contributor.authorSutton, A.-
dc.contributor.authorNeumann, F.-
dc.contributor.authorO'Reilly, U.-
dc.contributor.editorSoule, T.-
dc.identifier.citationProceedings of the 14th International Conference on Genetic and Evolutionary Computation, held in Philadelphia, Pennsylvania, 7-11 July, 2012 / T. Soule (ed.): pp.1333-1340-
dc.description.abstractThis paper contributes to the rigorous understanding of genetic programming algorithms by providing runtime complexity analyses of the well-studied Max problem. Several experimental studies have indicated that it is hard to solve the Max problem with crossover-based algorithms. Our analyses show that different variants of the Max problem can provably be solved using simple mutation-based genetic programming algorithms. Our results advance the body of computational complexity analyses of genetic programming, indicate the importance of mutation in genetic programming, and reveal new insights into the behavior of mutation-based genetic programming algorithms.-
dc.description.statementofresponsibilityTimo Kötzing, Andrew M. Sutton, Frank Neumann and Una-May O'Reilly-
dc.publisherAssociation for Computing Machinery-
dc.rightsCopyright 2012 ACM-
dc.subjectGenetic programming-
dc.subjectruntime analysis-
dc.titleThe max problem revisited: the importance of mutation in genetic programming-
dc.typeConference paper-
dc.contributor.conferenceGenetic and Evolutionary Computation Conference (14th : 2012 : Philadelphia, Pennsylvania)-
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]-
Appears in Collections:Aurora harvest 4
Computer Science publications

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