- Tytuł:
- Evolutionary computation based on Bayesian classifiers
- Autorzy:
-
Miquelez, T.
Bengoetxea, E.
Larranaga, P. - Powiązania:
- https://bibliotekanauki.pl/articles/907630.pdf
- Data publikacji:
- 2004
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
rozumowanie probabilistyczne
obliczenia ewolucyjne
sieć Bayesa
estymacja algorytmu dystrybucji
hybrid soft computing
probabilistic reasoning
evolutionary computing
classification
optimization
Bayesian networks
estimation of distribution algorithms - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 335-349
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł