Metaheuristic Procedures for Training Neural Networks

Overview

This book provides successful implementations of metaheuristic methods for neural network training. It is the first book to achieve this objective. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own. Overall, the book's aim is to provide a broad coverage of the concepts, methods, and tools of the important area of ANNs within the realm of continuous optimization.

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Paperback (Softcover reprint of hardcover 1st ed. 2006)
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Overview

This book provides successful implementations of metaheuristic methods for neural network training. It is the first book to achieve this objective. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own. Overall, the book's aim is to provide a broad coverage of the concepts, methods, and tools of the important area of ANNs within the realm of continuous optimization.

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Editorial Reviews

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"The strength of the book is its clear motivation to bring a new breath from metaheuristics into training of neural networks and integrate both sub-disciplines for the purpose of better exploitation of artificial intelligence approaches. … The most benefiting reader of this book will perhaps be those who research on modelling data with ANN faced with difficulty of robust mapping with classical training algorithms." (S. Gazioglu, Journal of the Operational Research Society, Vol. 58 (12), 2007)

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Product Details

  • ISBN-13: 9781441941282
  • Publisher: Springer US
  • Publication date: 11/19/2010
  • Series: Operations Research/Computer Science Interfaces Series , #35
  • Edition description: Softcover reprint of hardcover 1st ed. 2006
  • Edition number: 1
  • Pages: 252
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.56 (d)

Table of Contents

Classical Training Methods.- Local Search Based Methods.- Simulated Annealing.- Tabu Search.- Variable Neighbourhood Search.- Population Based Methods.- Estimation of Distribution Algorithms.- Genetic Algorithms.- Scatter Search.- Other Advanced Methods.- Ant Colony Optimization.- Cooperative Coevolutionary Methods.- Greedy Randomized Adaptive Search Procedures.- Memetic Algorithms.

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