rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Estimation of Distribution Algorithms
    sub title: a New Tool for Evolutionary Computation
  name:
    name part: Larrañaga, Pedro.
    role:
      role term: creator
  name:
    name part: Lozano, José A.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    date issued: 2002
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (416 pages)
  abstract: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face…
  note: edited by Pedro Larrañaga, Jose A. Lozano.
  note: Electronic resource.
  subject:
    topic: Evolutionary programming (Computer science)
  subject:
    topic: Evolutionary computation
  subject:
    topic: Evolutionary computation
  subject:
    topic: Evolutionary programming (Computer science)
  classification: QA76.618
  classification: 005.1
  related item:
    title info:
      title: Genetic algorithms and evolutionary computation ; 2
  identifier: 9781461515395 (electronic bk.)
  identifier: 1461515394 (electronic bk.)
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: I9W
    record creation date: 121227
    record change date: 20260422224657.8
    record identifier: in00003540775
    language of cataloging:
      language term: eng