© 2006

Metaheuristic Procedures for Training Neutral Networks

  • Enrique Alba
  • Rafael Martí

Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 36)

Table of contents

  1. Front Matter
    Pages i-4
  2. Introduction

    1. Emilio Soria, José David Martín, Paulo J. G. Lisboa
      Pages 7-36
  3. Local Search Based Methods

    1. E. Aarts, P. van der Horn, J. Korst, W. Michiels, H. Sontrop
      Pages 37-52
    2. Fred Glover, Rafael Marti
      Pages 53-69
    3. José Andrés Moreno Pérez, Nenad Mladenović, Belén Melián Batista, Ignacio J. García del Amo
      Pages 71-86
  4. Population Based Methods

    1. Julio Madera, Bernabé Dorronsoro
      Pages 87-108
    2. Enrique Alba, Francisco Chicano
      Pages 109-137
    3. Manuel Laguna, Rafael Martí
      Pages 139-152
  5. Other Advanced Methods

    1. Krzysztof Socha, Christian Blum
      Pages 153-180
    2. Nicolás García-Pedrajas, César Hervás-Martínez, Domingo Ortiz-Boyer
      Pages 181-206
    3. Francisco R. Angel-Bello, José Luis González-Velarde, Ada M. Alvarez
      Pages 207-223
    4. Natalio Krasnogor, Alberto Aragón, Joaquín Pacheco
      Pages 225-248
  6. Back Matter
    Pages 249-252

About this book


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.


Approximation algorithm algorithms artificial intelligence distribution genetic algorithms metaheuristic neural network optimization

Editors and affiliations

  • Enrique Alba
    • 1
  • Rafael Martí
    • 2
  1. 1.Universidad de MalagaMalagaSpain
  2. 2.Univeristat de ValenciaBurjassotSpain

Bibliographic information


From the reviews:

"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)