Metaheuristic Procedures for Training Neural Networks

Enrique Alba, Rafael Marti - Collection Operation research/Computer science interfaces series

252 pages, parution le 30/06/2006

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Résumé

Artificial neural networks (ANNs) offer a general framework for representing non-linear mappings from several input variables to several output variables, and they can be considered as an extension of the many conventional mapping techniques. In addition to many considerations on their biological foundations and their really wide spectrum of applications, constructing appropriate ANNs can be seen as a really hard problem. A distinguished task in building ANNs is the tuning of a set of parameters known as weights. This will be the main focus of the present book. The trained ANNs can be later used in classification (or recognition) problems, where the ANN outputs represent categories, or in prediction (approximation) problems, where the outputs represent continuous variables.

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, in which classical training methods are reviewed for the sake of the book's completeness, we have classified the chapters in three main categories. The first one is devoted to local search based methods, in which we include Simulated Annealing, Tabu Search, and Variable Neighborhood Search. The second part of the book presents the most effective population based methods, such as Estimation Distribution algorithms, Scatter Search, and Genetic Algorithms. Finally, the third part includes other advanced techniques, such as Ant Colony Optimization, Co-evolutionary methods, GRASP, and Memetic algorithms. All these methods have been shown to work out high quality solutions in a wide range of hard optimization problems. However, 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.

Written for: Researchers, students, and technically competent practitioners not only in neural networks, but also in Management Science, Industrial Engineering, Economics, and Computer Science

Sommaire

Introduction
- Classical training methods
Local search based methods
- Simulated annealing
- Tabu search
- Variable neighbourghood search
Population based methods
- Estimation of distribution algorithms
- Genetic algorithms
- Scatter search
Other advanced methods
- Ant colony optimization
- Cooperative coevolutionary methods
- Greedy randomized adaptative search procedures
- Memetic algorithms

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Enrique Alba, Rafael Marti
Collection	Operation research/Computer science interfaces series
Parution	30/06/2006
Nb. de pages	252
Format	16 x 24
Couverture	Relié
Poids	565g
Intérieur	Noir et Blanc
EAN13	9780387334158
ISBN13	978-0-387-33415-8