Probabilistic model-building genetic algorithms

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

@InProceedings{Pelikan:2011:GECCOcomp,
  author =       "Martin Pelikan",
  title =        "Probabilistic model-building genetic algorithms",
  booktitle =    "GECCO 2011 Late breaking abstracts",
  year =         "2011",
  editor =       "Christian Blum",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "913--940",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002120",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Probabilistic model-building algorithms (PMBGAs)
                 replace traditional variation of genetic and
                 evolutionary algorithms by (1) building a probabilistic
                 model of promising solutions and (2) sampling the built
                 model to generate new candidate solutions. PMBGAs are
                 also known as estimation of distribution algorithms
                 (EDAs) and iterated density-estimation algorithms
                 (IDEAs).

                 Replacing traditional crossover and mutation operators
                 by building and sampling a probabilistic model of
                 promising solutions enables the use of machine learning
                 techniques for automatic discovery of problem
                 regularities and exploitation of these regularities for
                 effective exploration of the search space. Using
                 machine learning in optimisation enables the design of
                 optimisation techniques that can automatically adapt to
                 the given problem. There are many successful
                 applications of PMBGAs, for example, Ising spin glasses
                 in 2D and 3D, graph partitioning, MAXSAT, feature
                 subset selection, forest management, groundwater
                 remediation design, telecommunication network design,
                 antenna design, and scheduling.

                 The tutorial Probabilistic Model-Building GAs will
                 provide a gentle introduction to PMBGAs with an
                 overview of major research directions in this area.
                 Strengths and weaknesses of different PMBGAs will be
                 discussed and suggestions will be provided to help
                 practitioners to choose the best PMBGA for their
                 problem.",
  notes =        "Also known as \cite{2002120} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Martin Pelikan

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