Self-configuring crossover

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Goldman:2011:GECCOcomp,
  author =       "Brian W. Goldman and Daniel R. Tauritz",
  title =        "Self-configuring crossover",
  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 =        "575--582",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002051",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Crossover is a core genetic operator in many
                 evolutionary algorithms (EAs). The performance of such
                 EAs on a given problem is dependent on properly
                 configuring crossover. A small set of common crossover
                 operators is used in the vast majority of EAs,
                 typically fixed for the entire evolutionary run.
                 Selecting which crossover operator to use and tuning
                 its associated parameters to obtain acceptable
                 performance on a specific problem often is a time
                 consuming manual process. Even then a custom crossover
                 operator may be required to achieve optimal
                 performance. Finally, the best crossover configuration
                 may be dependent on the state of the evolutionary
                 run.

                 This paper introduces the Self-Configuring Crossover
                 operator encoded with linear genetic programming which
                 addresses these shortcomings while relieving the user
                 from the burden of crossover configuration. To
                 demonstrate its general applicability, the novel
                 crossover operator was applied without any problem
                 specific tuning. Results are presented showing it to
                 outperform the traditional crossover operators
                 arithmetic crossover, uniform crossover, and n-point
                 crossover on the Rosenbrock, Rastrigin, Offset
                 Rastrigin, DTrap, and NK Landscapes benchmark
                 problems.",
  notes =        "Also known as \cite{2002051} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Brian W Goldman Daniel R Tauritz

Citations