# Evolutionary computation based on Bayesian classifiers

Teresa Miquélez; Endika Bengoetxea; Pedro Larrañaga

International Journal of Applied Mathematics and Computer Science (2004)

- Volume: 14, Issue: 3, page 335-349
- ISSN: 1641-876X

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topMiquélez, Teresa, Bengoetxea, Endika, and Larrañaga, Pedro. "Evolutionary computation based on Bayesian classifiers." International Journal of Applied Mathematics and Computer Science 14.3 (2004): 335-349. <http://eudml.org/doc/207701>.

@article{Miquélez2004,

abstract = {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.},

author = {Miquélez, Teresa, Bengoetxea, Endika, Larrañaga, Pedro},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {probabilistic reasoning; optimization; evolutionary computing; classification; estimation of distribution algorithms; Bayesian networks; hybrid soft computing},

language = {eng},

number = {3},

pages = {335-349},

title = {Evolutionary computation based on Bayesian classifiers},

url = {http://eudml.org/doc/207701},

volume = {14},

year = {2004},

}

TY - JOUR

AU - Miquélez, Teresa

AU - Bengoetxea, Endika

AU - Larrañaga, Pedro

TI - Evolutionary computation based on Bayesian classifiers

JO - International Journal of Applied Mathematics and Computer Science

PY - 2004

VL - 14

IS - 3

SP - 335

EP - 349

AB - 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.

LA - eng

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

UR - http://eudml.org/doc/207701

ER -

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