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IJON
2008

Evolutionary ensemble of diverse artificial neural networks using speciation

13 years 3 months ago
Evolutionary ensemble of diverse artificial neural networks using speciation
Recently, many researchers have designed neural network architectures with evolutionary algorithms but most of them have used only the fittest solution of the last generation. To better exploit information, an ensemble of individuals is a more promising choice because information that is derived from combining a set of classifiers might produce higher accuracy than merely using the information from the best classifier among them. One of the major factors for optimum accuracy is the diversity of the classifier set. In this paper, we present a method of generating diverse evolutionary neural networks through fitness sharing and then combining these networks by the behavior knowledge space method. Fitness sharing that shares resources if the distance between the individuals is smaller than the sharing radius is a representative speciation method, which produces diverse results than standard evolutionary algorithms that converge to only one solution. Especially, the proposed method calcul...
Kyung-Joong Kim, Sung-Bae Cho
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where IJON
Authors Kyung-Joong Kim, Sung-Bae Cho
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