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Evolutionary design of MLP neural network architectures

Content Provider	IEEE Xplore Digital Library
Author	Filho, E.F.M. de Carvalho, A.
Copyright Year	1997
Description	Author affiliation: Dept. of Comput. Sci. & Stat., Sao Paulo Univ., Brazil (Filho, E.F.M.)
Abstract	In neural networks design, some parameters must be adequately set for an efficient performance to be achieved. The setting of these parameters is not a trivial task since different applications may require different values. The "trial-and-error" or traditional engineering approaches for this task do not guarantee that an optimal set of parameters is found. Evolutionary approaches have been recently proposed to overcome these problems. Genetic algorithms have been used as a heuristic search technique to find adequate neural architectures. This technique is based on natural selection and genetic mechanisms. It tries to produce better individuals from an initial set of individuals, based on a given criteria. This paper presents some results achieved by using this technique to search optimal neural architectures to solve real world credit analysis problems. In this paper the searches have been restricted to multilayer perceptron (MLP) networks which are feedforward fully-connected strictly-layered architectures.
Starting Page	58
Ending Page	65
File Size	762434
Page Count	8
File Format	PDF
ISBN	0818680709
DOI	10.1109/SBRN.1997.645849
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-12-03
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Genetic algorithms Multilayer perceptrons Biological neural networks Network topology Humans Evolution (biology) Computer architecture Computational intelligence Laboratories
Content Type	Text
Resource Type	Article

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