This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation. An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated.
Product Identifiers
Publisher
World Scientific Publishing Co Pte Ltd
ISBN-13
9789810231064
eBay Product ID (ePID)
95665237
Product Key Features
Author
E Vonk, R P Johnson, Lakhmi C Jain
Publication Name
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Format
Hardcover
Language
English
Subject
Computer Science
Publication Year
1997
Type
Textbook
Number of Pages
192 Pages
Dimensions
Volume
14
Additional Product Features
Title_Author
R P Johnson, Lakhmi C Jain, E Vonk
Series Title
Advances in Fuzzy Systems-Applications and Theory
Country/Region of Manufacture
Singapore
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