Metaheuristic Procedures for Training Neural Networks (Hardcover, 2006 ed.)

Metaheuristic Procedures for Training Neural Networks provides successful implementations of metaheuristic methods for neural network training. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own. Apart from Chapter 1, which reviews classical training methods, the chapters are divided into three main categories. The first one is devoted to local search based methods, including Simulated Annealing, Tabu Search, and Variable Neighborhood Search. The second part of the book presents population based methods, such as Estimation Distribution algorithms, Scatter Search, and Genetic Algorithms. The third part covers other advanced techniques, such as Ant Colony Optimization, Co-evolutionary methods, GRASP, and Memetic algorithms. Overall, the book's objective is engineered to provide a broad coverage of the concepts, methods, and tools of this important area of ANNs within the realm of continuous optimization.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Operations Research/Computer Science Interfaces Series, 35
Release date:	May 2006
First published:	2006
Editors:	Enrique Alba • Rafael Marti
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Hardcover
Pages:	252
Edition:	2006 ed.
ISBN-13:	978-0-387-33415-8
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks Promotions
LSN:	0-387-33415-7
Barcode:	9780387334158

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