Evolutionary computation based on Bayesian classifiers

• Autorzy: Teresa Miquélez , Endika Bengoetxea , Pedro Larrañaga
• 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.

Słowa kluczowe

EN

probabilistic reasoning; optimization; evolutionary computing; classification; estimation of distribution algorithms; Bayesian networks; hybrid soft computing

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2004
• Tom: 14
• Numer: 3
• Strony: 335-349

Daty

wydano

2004

otrzymano

2004-03-01

poprawiono

2004-06-01

Twórcy

autor

Teresa Miquélez

• Department of Computer Architecture and Technology, University of the Basque Country, P.O. Box 649, 20080 San Sebastian, Spain

autor

Endika Bengoetxea

• Department of Computer Architecture and Technology, University of the Basque Country, P.O. Box 649, 20080 San Sebastian, Spain

autor

Pedro Larrañaga

• Department of Computer Science and Artificial Intelligence, University of the Basque Country, P.O. Box 649, 20080 San Sebastian, Spain

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