Functional Genetic Programming and Exhaustive Program Search with Combinator Expressions

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

  author =       "Forrest Briggs and Melissa O'Neill",
  title =        "Functional Genetic Programming and Exhaustive Program
                 Search with Combinator Expressions",
  journal =      "International Journal of Knowledge-Based and
                 Intelligent Engineering Systems",
  year =         "2008",
  volume =       "12",
  number =       "1",
  pages =        "47--68",
  keywords =     "genetic algorithms, genetic programming,
  ISSN =         "1327-2314",
  publisher =    "IOS Press",
  URL =          "",
  DOI =          "doi:10.3233/KES-2008-12105",
  size =         "22 page",
  abstract =     "Using a strongly typed functional programming language
                 for genetic programming has many advantages, but
                 evolving functional programs with variables requires
                 complex genetic operators with special cases to avoid
                 creating ill-formed programs. We introduce combinator
                 expressions as an alternative program representation
                 for genetic programming, providing the same expressive
                 power as strongly typed functional programs, but in a
                 simpler format that avoids variables and other
                 syntactic clutter. We outline a complete
                 genetic-programming system based on combinator
                 expressions, including a novel generalised genetic
                 operator, and also show how it is possible to
                 exhaustively enumerate all well-typed combinator
                 expressions up to a given size.

                 Our experimental evidence shows that combinator
                 expressions compare favourably with prior
                 representations for functional genetic programming and
                 also offers insight into situations where exhaustive
                 enumeration outperforms genetic programming and vice
  notes =        "Standard ML and Haskell mentioned a few times.
                 even-N-parity, stacks and queues. KES",

Genetic Programming entries for Forrest S Briggs Melissa E O'Neill