GA or GP? That is not the question

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

  author =       "John Woodward",
  title =        "{GA} or {GP}? That is not the question",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "1056--1063",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, Computer
                 science, Evolutionary computation, High performance
                 computing, Terminology, Tree data structures, data
                 structures, search problems, GA, GP, No Free Lunch
                 theorem, evolutionary computation, fixed length linear
                 representation, variable size tree representation",
  ISBN =         "0-7803-7804-0",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2003.1299785",
  size =         "8 pages",
  abstract =     "Genetic Algorithms (GAs) and Genetic Programming (GP)
                 are often considered as separate but related fields.
                 Typically, GAs use a fixed length linear
                 representation, whereas GP uses a variable size tree
                 representation. This paper argues that the differences
                 are unimportant. Firstly, variable length actually
                 means variable length up to some fixed limit, so can
                 really be considered as fixed length. Secondly, the
                 representations and genetic operators of GA and GP
                 appear different, however ultimately it is a population
                 of bit strings in the computers memory which is being
                 manipulated whether it is GA or GP which is being run
                 on the computer.

                 The important difference lies in the interpretation of
                 the representation; if there is a one to one mapping
                 between the description of an object and the object
                 itself (as is the case with the representation of
                 numbers), or a many to one mapping (as is the case with
                 the representation of programs). This has ramifications
                 for the validity of the No Free Lunch theorem, which is
                 valid in the first case but not in the second. It is
                 argued that due to the highly related nature of GAs and
                 GP, that many of the empirical results discovered in
                 one field will apply to the other field, for example
                 maintaining high diversity in a population to improve
  notes =        "

                 CEC 2003 - A joint meeting of the IEEE, the IEAust, the
                 EPS, and the IEE.",

Genetic Programming entries for John R Woodward