
Automatic Generation Of Neural Network Architecture Using Evolutionary Computation
World Scientific Publishing Co Pte Ltd
Will be published approx. on 4. November 1997
Book
Hardback
192 pages
978-981-02-3106-4 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Singapore
Singapore
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
ISBN-13
978-981-02-3106-4 (9789810231064)
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Schweitzer Classification
Persons
Author
Australian Defence Sci & Tech, Australia
Univ Of South Australia, Australia
Vrije Univ, Amsterdam, The Netherlands
Content
Artificial neural networks; evolutionary computation; the biological background; mathematical foundations of genetic algorithms; implementing gas; hybridization of evolutionary computation and neural networks; using genetic programming to generate neural networks; using a GA to optimize the weights of a neural network; using a GA with grammar encoding to generate neural networks; conclusions and future directions.