A Comparative Study of Different Grammar-based Genetic Programming Approaches

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Nuno Lourenco and Joaquim Ferrer and 
                 Francisco B. Pereira and Ernesto Costa",
  title =        "A Comparative Study of Different Grammar-based Genetic
                 Programming Approaches",
  booktitle =    "EuroGP 2017: Proceedings of the 20th European
                 Conference on Genetic Programming",
  year =         "2017",
  month =        "19-21 " # apr,
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  series =       "LNCS",
  volume =       "10196",
  publisher =    "Springer Verlag",
  address =      "Amsterdam",
  pages =        "311--325",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution: Poster",
  DOI =          "doi:10.1007/978-3-319-55696-3_20",
  abstract =     "Grammars are useful formalisms to specify constraints,
                 and not surprisingly, they have attracted the attention
                 of Evolutionary Computation (EC) researchers to enforce
                 problem restrictions. Context-Free-Grammar GP (CFG-GP)
                 established the foundations for the application of
                 grammars in Genetic Programming (GP), whilst
                 Grammatical Evolution (GE) popularised the use of these
                 approaches, becoming one of the most used GP variants.
                 However, studies have shown that GE suffers from issues
                 that have impact on its performance. To minimise these
                 issues, several extensions have been proposed, which
                 made the distinction between GE and CFG-GP less
                 noticeable. Another direction was followed by
                 Structured Grammatical Evolution (SGE) that maintains
                 the separation between genotype and phenotype from GE,
                 but overcomes most of its issues. Our goal is to
                 perform a comparative study between CFG-GP, GE and SGE
                 to examine their relative performance. The results show
                 that in most of the selected benchmarks, CFG-GP and SGE
                 have a similar performance, showing that SGE is a good
                 alternative to GE.",
  notes =        "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and

Genetic Programming entries for Nuno Lourenco Joaquim Ferrer Francisco Jose Baptista Pereira Ernesto Costa