Free Delivery Over $100
|
|
|||
|
||||
OverviewThis 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. Full Product DetailsAuthor: R P Johnson (Australian Defence Sci & Tech, Australia) , Lakhmi C Jain (Univ Of South Australia, Australia) , E Vonk (Vrije Univ, Amsterdam, The Netherlands)Publisher: World Scientific Publishing Co Pte Ltd Imprint: World Scientific Publishing Co Pte Ltd Volume: 14 ISBN: 9789810231064ISBN 10: 9810231067 Pages: 192 Publication Date: 04 November 1997 Audience: College/higher education , Professional and scholarly , Undergraduate , Postgraduate, Research & Scholarly Format: Hardback Publisher's Status: Active Availability: To order  Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us. Table of ContentsArtificial 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.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
||||
