A Grammar Design Pattern for Arbitrary Program Synthesis Problems in Genetic Programming

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

  author =       "Stefan Forstenlechner and David Fagan and 
                 Miguel Nicolau and Michael O'Neill",
  title =        "A Grammar Design Pattern for Arbitrary Program
                 Synthesis Problems in Genetic Programming",
  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 =        "262--277",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming, G3P, PushGP,
                 Python: Poster",
  DOI =          "doi:10.1007/978-3-319-55696-3_17",
  size =         "16 pages",
  abstract =     "Grammar Guided Genetic Programming has been applied to
                 many problem domains. It is well suited to tackle
                 program synthesis, as it has the capability to evolve
                 code in arbitrary languages. Nevertheless, grammars
                 designed to evolve code have always been tailored to
                 specific problems resulting in bespoke grammars, which
                 makes them difficult to reuse. In this study a more
                 general approach to grammar design in the program
                 synthesis domain is presented. The approach undertaken
                 is to create a grammar for each data type of a language
                 and combine these grammars for the problem at hand,
                 without having to tailor a grammar for every single
                 problem. The approach can be applied to arbitrary
                 problem instances of program synthesis and can be used
                 with any programming language. The approach is also
                 extensible to use libraries available in a given
                 language. The grammars presented can be applied to any
                 grammar-based Genetic Programming approach and make it
                 easy for researches to rerun experiments or test new
                 problems. The approach is tested on a suite of
                 benchmark problems and compared to PushGP, as it is the
                 only GP system that has presented results on a wide
                 range of benchmark problems. The object of this study
                 is to match or outperform PushGP on these problems
                 without tuning grammars to solve each specific
  notes =        "cites \cite{McKay:2010:GPEM}

                 Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and

Genetic Programming entries for Stefan Forstenlechner David Fagan Miguel Nicolau Michael O'Neill