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2004 | 14 | 3 | 335-349
Tytuł artykułu

Evolutionary computation based on Bayesian classifiers

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier-either naive Bayes, seminaive Bayes, tree augmented naive Bayes or a similar one-is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature.
Rocznik
Tom
14
Numer
3
Strony
335-349
Opis fizyczny
Daty
wydano
2004
otrzymano
2004-03-01
poprawiono
2004-06-01
Twórcy
  • Department of Computer Architecture and Technology, University of the Basque Country, P.O. Box 649, 20080 San Sebastian, Spain
  • Department of Computer Architecture and Technology, University of the Basque Country, P.O. Box 649, 20080 San Sebastian, Spain
  • Department of Computer Science and Artificial Intelligence, University of the Basque Country, P.O. Box 649, 20080 San Sebastian, Spain
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv14i3p335bwm
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