Titlebook: Metaheuristic Procedures for Training Neural Networks; Enrique Alba,Rafael Martí Book 2006 Springer-Verlag US 2006 Approximation.algorithm

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Enrique Alba,Rafael MartíApart from research efforts bringing together metaheuristic techniques to train artificial neural networks, this is the first book to achieve this objective. This book provides a unified approach to t

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Metaheuristic Procedures for Training Neural Networks978-0-387-33416-5Series ISSN 1387-666X Series E-ISSN 2698-5489

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Memetic Algorithms landscapes with many poor local optimae. The aim of our work is to design a training strategy that is able to cope with difficult error manyfolds, and to quickly deliver trained neural networks that produce small errors. A method such as the one we proposed might also be used as an “online” training strategy.

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Book 2006ver, 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 b

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Tabu Searchckpropagation, other optimization methods such as tabu search have been applied to solve this problem. This chapter describes two training algorithms based on the tabu search. The experimentation shows that the procedures provide high quality solutions to the training problem, and in addition consume a reasonable computational effort.