Metaheuristic Procedures for Training Neutral Networks [electronic resource] / edited by Enrique Alba, Rafael Martȡ.

Por: Alba, Enrique [editor.]Colaborador(es): Martȡ, Rafael [editor.]Tipo de material: TextoTextoSeries Operations Research/Computer Science Interfaces Series, 36Editor: Boston, MA : Springer US, 2006Descripción: XII, 252 p. 65 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9780387334165Trabajos contenidos: SpringerLink (Online service)Tema(s): Economics | Computer science -- Mathematics | Mathematical optimization | Operations research | Business logistics | Economics/Management Science | Operations Research/Decision Theory | Optimization | Mathematical Modeling and Industrial Mathematics | Operations Research, Mathematical Programming | Production/Logistics | Computational Mathematics and Numerical AnalysisFormatos físicos adicionales: Sin títuloClasificación CDD: 658.40301 Clasificación LoC:HD30.23Recursos en línea: de clik aquí para ver el libro electrónico
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Springer eBooksResumen: 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.
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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.

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.

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