Automatically designing selection heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Woodward:2011:GECCOcomp,
  author =       "John Robert Woodward and Jerry Swan",
  title =        "Automatically designing selection heuristics",
  booktitle =    "GECCO 2011 1st workshop on evolutionary computation
                 for designing generic algorithms",
  year =         "2011",
  editor =       "Gisele L. Pappa and Alex A. Freitas and Jerry Swan and 
                 John Woodward",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "583--590",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002052",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In a standard evolutionary algorithm such as genetic
                 algorithms (GAs), a selection mechanism is used to
                 decide which individuals are to be chosen for
                 subsequent mutation. Examples of selection mechanisms
                 are fitness-proportional selection, in which
                 individuals are chosen with a probability directly in
                 proportion to their fitness value, and rank selection,
                 in which individuals are selected with a probability in
                 proportion to their ordinal ranking by fitness. These
                 two human-designed selection heuristics implicitly
                 assume that fitter individuals produce fitter
                 offspring. Whilst one might invest human ingenuity in
                 the construction of alternative selection heuristics,
                 the approach adopted in this paper is to represent a
                 generic family of selection heuristics which are
                 applied via an algorithmic framework. We then generate
                 instances of selection heuristics and test their
                 performance in an evolutionary algorithm (which in this
                 paper tackles a variety of bitstring optimization
                 problems). The representation we use for the program
                 space is a register machine (a set of real-valued
                 registers on which a program is executed).
                 Fitness-proportional and rank selection can be
                 expressed as one-line programs, and more sophisticated
                 selection heuristics may also be expressed. The result
                 is a system which produces selection heuristics that
                 outperform either of the original selection
                 heuristics.",
  notes =        "Also known as \cite{2002052} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for John R Woodward Jerry Swan

Citations