Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications

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

@Book{Affenzeller:GAGP,
  author =       "Michael Affenzeller and Stephan Winkler and 
                 Stefan Wagner and Andreas Beham",
  title =        "Genetic Algorithms and Genetic Programming: Modern
                 Concepts and Practical Applications",
  publisher =    "CRC Press",
  year =         "2009",
  series =       "Numerical Insights",
  address =      "Singapore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-58488-629-3",
  URL =          "http://gagp2009.heuristiclab.com/",
  URL =          "http://www.crcpress.com/product/isbn/9781584886297",
  abstract =     "Genetic Algorithms and Genetic Programming: Modern
                 Concepts and Practical Applications discusses
                 algorithmic developments in the context of genetic
                 algorithms (GAs) and genetic programming (GP). It
                 applies the algorithms to significant combinatorial
                 optimisation problems and describes structure
                 identification using HeuristicLab as a platform for
                 algorithm development. The book focuses on both
                 theoretical and empirical aspects. The theoretical
                 sections explore the important and characteristic
                 properties of the basic GA as well as main
                 characteristics of the selected algorithmic extensions
                 developed by the authors. In the empirical parts of the
                 text, the authors apply GAs to two combinatorial
                 optimisation problems: the traveling salesman and
                 capacitated vehicle routing problems. To highlight the
                 properties of the algorithmic measures in the field of
                 GP, they analyze GP-based nonlinear structure
                 identification applied to time series and
                 classification problems.

                 Written by core members of the HeuristicLab team, this
                 book provides a better understanding of the basic
                 workflow of GAs and GP, encouraging readers to
                 establish new bionic, problem-independent theoretical
                 concepts. By comparing the results of standard GA and
                 GP implementation with several algorithmic extensions,
                 it also shows how to substantially increase achievable
                 solution quality.",
  notes =        "Reviewed in \cite{Pappa:2009:GPEM}. My copy missing
                 pages i to vi.

                 ",
  size =         "379 pages",
}

Genetic Programming entries for Michael Affenzeller Stephan M Winkler Stefan Wagner Andreas Beham

Citations