Polytypic Genetic Programming

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

  author =       "Jerry Swan and Krzysztof Krawiec and Neil Ghani",
  title =        "Polytypic Genetic Programming",
  booktitle =    "20th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2017",
  editor =       "Giovanni Squillero",
  series =       "LNCS",
  volume =       "10200",
  publisher =    "Springer",
  pages =        "66--81",
  address =      "Amsterdam",
  month =        "19-21 " # apr,
  organisation = "Species",
  keywords =     "genetic algorithms, genetic programming, genetic
                 Improvement, Polytypic programming, Datatype generic
                 programming, Functional programming, Scala, EBNF
                 grammar, ADT, 6-mux",
  URL =          "http://eprints.whiterose.ac.uk/117964/",
  URL =          "http://eprints.whiterose.ac.uk/117964/1/polytypic_genetic_programming.pdf",
  DOI =          "doi:10.1007/978-3-319-55792-2_5",
  size =         "16 pages",
  abstract =     "Program synthesis via heuristic search often requires
                 a great deal of boilerplate code to adapt program APIs
                 to the search mechanism. In addition, the majority of
                 existing approaches are not type-safe: i.e. they can
                 fail at runtime because the search mechanisms lack the
                 strict type information often available to the
                 compiler. In this article, we describe Polytope, a
                 Scala framework that uses polytypic programming, a
                 relatively recent advance in program abstraction.
                 Polytope requires a minimum of boilerplate code and
                 supports a form of strong-typing in which type rules
                 are automatically enforced by the compiler, even for
                 search operations such as mutation which are applied at
                 run-time. By operating directly on language-native
                 expressions, it provides an embeddable optimization
                 procedure for existing code. We give a tutorial example
                 of the specific polytypic approach we adopt and compare
                 both runtime efficiency and required lines of code
                 against the well-known EpochX GP framework, showing
                 comparable performance in the former and the complete
                 elimination of boilerplate for the latter.",
  notes =        "http://www.epochx.org/javadoc/1.4/

                 EvoApplications2017 held in conjunction with
                 EuroGP'2017, EvoCOP2017 and EvoMusArt2017

Genetic Programming entries for Jerry Swan Krzysztof Krawiec Neil Ghani