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

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@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