Metaheuristic Procedures for Training Neural Networks

Enrique Alba

ISBN 10: 0387334157 / ISBN 13: 9780387334158

Edité par Springer Us Mai 2006, 2006

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9780387334158

Détails bibliographiques

• Titre: Metaheuristic Procedures for Training Neural Networks
• Éditeur: Springer Us Mai 2006
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 0387334157
• ISBN à 13 chiffres: 9780387334158
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 242 L x 163 H x 19 P (en millimètres) 242 millimètres de largeur par 163 millimètres de hauteur par 19 millimètres de profondeur
• Poids: 558 grammes

A propos de ce titre

Synopsis

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.