Experimental Supplements to the Computational Complexity Analysis of Genetic Programming for Problems Modelling Isolated Program Semantics

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@InProceedings{conf/ppsn/UrliWN12,

• author = "Tommaso Urli and Markus Wagner and Frank Neumann",
• title = "Experimental Supplements to the Computational Complexity Analysis of Genetic Programming for Problems Modelling Isolated Program Semantics",
• booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 1)",
• year = "2012",
• editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone",
• volume = "7491",
• series = "Lecture Notes in Computer Science",
• pages = "102--112",
• address = "Taormina, Italy",
• month = sep # " 1-5",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, problem complexity, multiple objective optimisation, experimental evaluation",
• isbn13 = "978-3-642-32936-4",
• DOI = "doi:10.1007/978-3-642-32937-1_11",
• size = "11 pages",
• abstract = "In this paper, we carry out experimental investigations that complement recent theoretical investigations on the runtime of simple genetic programming algorithms [3, 7]. Crucial measures in these theoretical analyses are the maximum tree size that is attained during the run of the algorithms as well as the population size when dealing with multi-objective models. We study those measures in detail by experimental investigations and analyse the runtime of the different algorithms in an experimental way.",
• notes = "Cited by \cite{Nguyen:2013:foga}",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• affiliation = "DIEGM, Universita degli Studi di Udine, 33100 Udine, Italy",

}

Genetic Programming entries for Tommaso Urli Markus Wagner Frank Neumann

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