Genetics-based machine learning

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

  author =       "Tim Kovacs",
  title =        "Genetics-based machine learning",
  booktitle =    "Handbook of Natural Computing",
  publisher =    "Springer",
  year =         "2012",
  editor =       "Grzegorz Rozenberg and Thomas Baeck and Joost N. Kok",
  volume =       "2",
  pages =        "937--986",
  month =        "19 " # aug,
  keywords =     "genetic algorithms, genetic programming, Artificial
                 Intelligence, Machine Learning",
  isbn13 =       "978-3-540-92909-3",
  abstract-url = "",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/978-3-540-92910-9_30",
  abstract =     "This is a survey of the field of Genetics-based
                 Machine Learning (GBML): the application of
                 evolutionary algorithms to machine learning. We assume
                 readers are familiar with evolutionary algorithms and
                 their application to optimisation problems, but not
                 necessarily with machine learning. We briefly outline
                 the scope of machine learning, introduce the more
                 specific area of supervised learning, contrast it with
                 optimisation and present arguments for and against
                 GBML. Next we introduce a framework for GBML which
                 includes ways of classifying GBML algorithms and a
                 discussion of the interaction between learning and
                 evolution. We then review the following areas with
                 emphasis on their evolutionary aspects: GBML for
                 sub-problems of learning, genetic programming, evolving
                 ensembles, evolving neural networks, learning
                 classifier systems, and genetic fuzzy systems.",

Genetic Programming entries for Tim Kovacs