Evolving a Statistics Class Using Object Oriented Evolutionary Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Alexandros Agapitos and Simon M. Lucas",
  title =        "Evolving a Statistics Class Using Object Oriented
                 Evolutionary Programming",
  editor =       "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and 
                 Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
  booktitle =    "Proceedings of the 10th European Conference on Genetic
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "4445",
  year =         "2007",
  address =      "Valencia, Spain",
  month =        "11-13 " # apr,
  pages =        "291--300",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-71602-0",
  ISBN =         "3-540-71602-5",
  DOI =          "doi:10.1007/978-3-540-71605-1_27",
  abstract =     "Object Oriented Evolutionary Programming is used to
                 evolve programs that calculate some statistical
                 measures on a set of numbers. We compared this
                 technique with a more standard functional
                 representation. We also studied the effects of scalar
                 and Pareto-based multi-objective fitness functions to
                 the induction of multi-task programs. We found that the
                 induction of a program residing in an OO representation
                 space is more efficient, yielding less fitness
                 evaluations, and that scalar fitness performed better
                 than Pareto-based fitness in this problem domain.",
  notes =        "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
                 conjunction with EvoCOP2007, EvoBIO2007 and

Genetic Programming entries for Alexandros Agapitos Simon M Lucas