On the Automatic Design of a Representation for Grammar-based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Eric Medvet and Alberto Bartoli",
  title =        "On the Automatic Design of a Representation for
                 Grammar-based Genetic Programming",
  booktitle =    "EuroGP 2018: Proceedings of the 21st European
                 Conference on Genetic Programming",
  year =         "2018",
  month =        "4-6 " # apr,
  editor =       "Mauro Castelli and Lukas Sekanina and 
                 Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
  series =       "LNCS",
  volume =       "10781",
  publisher =    "Springer Verlag",
  address =      "Parma, Italy",
  pages =        "101--117",
  organisation = "EvoStar, Species",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Meta-evolution",
  isbn13 =       "978-3-319-77552-4",
  URL =          "http://www.human-competitive.org/sites/default/files/medvet-paper.pdf",
  DOI =          "doi:10.1007/978-3-319-77553-1_7",
  size =         "16 pages",
  abstract =     "A long-standing problem in Evolutionary Computation
                 consists in how to choose an appropriate representation
                 for the solutions. In this work we investigate the
                 feasibility of synthesizing a representation
                 automatically, for the large class of problems whose
                 solution spaces can be defined by a context-free
                 grammar. We propose a framework based on a form of
                 meta-evolution in which individuals are candidate
                 representations expressed with an ad hoc language that
                 we have developed to this purpose. Individuals compete
                 and evolve according to an evolutionary search aimed at
                 optimizing such representation properties as
                 redundancy, locality, uniformity of redundancy. We
                 assessed experimentally three variants of our framework
                 on established benchmark problems and compared the
                 resulting representations to human-designed
                 representations commonly used (e.g., classical
                 Grammatical Evolution). The results are promising in
                 the sense that the evolved representations indeed
                 exhibit better properties than the human-designed ones.
                 Furthermore, while those improved properties do not
                 result in a systematic improvement of search
                 effectiveness, some of the evolved representations do
                 improve search effectiveness over the human-designed
  notes =        "2018 HUMIES finalist

                 Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and

Genetic Programming entries for Eric Medvet Alberto Bartoli