Performance Models for Evolutionary Program Induction Algorithms based on Problem Difficulty Indicators

Created by W.Langdon from gp-bibliography.bib Revision:1.7975

@InProceedings{graff:2011:EuroGP,

• author = "Mario Graff and Riccardo Poli",
• title = "Performance Models for Evolutionary Program Induction Algorithms based on Problem Difficulty Indicators",
• booktitle = "Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011",
• year = "2011",
• month = "27-29 " # apr,
• editor = "Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado",
• series = "LNCS",
• volume = "6621",
• publisher = "Springer Verlag",
• address = "Turin, Italy",
• pages = "118--129",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• isbn13 = "978-3-642-20406-7",
• DOI = "doi:10.1007/978-3-642-20407-4_11",
• abstract = "Most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. In this paper, two models of evolutionary program-induction algorithms (EPAs) are proposed which overcome this limitation. We test our approach with two important classes of problems --- symbolic regression and Boolean function induction --- and a variety of EPAs including: different versions of genetic programming, gene expression programing, stochastic iterated hill climbing in program space and one version of cartesian genetic programming. We compare the proposed models against a practical model of EPAs we previously developed and find that in most cases the new models are simpler and produce better predictions. A great deal can also be learnt about an EPA via a simple inspection of our new models. E.g., it is possible to infer which characteristics make a problem difficult or easy for the EPA.",
• notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011",

}

Genetic Programming entries for Mario Graff Guerrero Riccardo Poli

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