Genetic Programming for Machine Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3989

@InCollection{Petrowski:2017:EAch6,
  author =       "Alain Petrowski and Sana Ben-Hamida",
  title =        "Genetic Programming for Machine Learning",
  booktitle =    "Evolutionary Algorithms",
  year =         "2017",
  publisher =    "John Wiley \& Sons, Inc.",
  chapter =      "6",
  pages =        "183--216",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, grammatical evolution, graph-based
                 representation, intrusion detection system, linear
                 genetic programming, linear-based representation,
                 machine learning, tree-based representation",
  isbn13 =       "9781119136378",
  URL =          "http://onlinelibrary.wiley.com/doi/10.1002/9781119136378.ch6/summary",
  DOI =          "doi:10.1002/9781119136378.ch6",
  size =         "34 pages",
  abstract =     "Genetic programming (GP) is considered as the
                 evolutionary technique having the widest range of
                 application domains. It can be used to solve problems
                 in at least three main fields: optimization, automatic
                 programming and machine learning. This chapter
                 summarizes the different GP implementations based on
                 one of the three representations: tree-based
                 representation, linear-based representation and
                 graph-based representation. It presents three of these
                 implementations that have proven successful in
                 practice: linear GP (LGP), grammatical evolution (GE)
                 for linear-based representation and Cartesian GP (CGP)
                 for graph-based representation. Several research papers
                 explore the feasibility of applying GP to
                 multi-category pattern classification problems. The
                 chapter proposes a CGP-based approach to design
                 classifiers for an Intrusion Detection problem. The
                 major problem faced by an intrusion detection system
                 (IDS) is the large number of false-positive alerts,
                 i.e. normal behaviours mistakenly classified as
                 alerts",
}

Genetic Programming entries for Alain Petrowski Sana Ben Hamida

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