Metaheuristic Procedures for Training Neural Networks / edited by Enrique Alba, Rafael Martí
(Operations Research/Computer Science Interfaces Series ; 35)
| Publisher | (New York, NY : Springer US : Imprint: Springer) |
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| Year | 2006 |
| Edition | 1st ed. 2006. |
| Authors | Alba, Enrique editor Martí, Rafael editor SpringerLink (Online service) |
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| Links to the text | Location | Volume | Call No. | Barcode No. | Status | Comments | ISBN | Printed | Restriction | Reserve |
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| Links to the text | Library Off-campus access |
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OB00164046 | Springer Business and Economics eBooks (電子ブック) | 9780387334165 |
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| Material Type | E-Book |
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| Media type | 機械可読データファイル |
| Size | XII, 252 p. 65 illus : online resource |
| Notes | 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 HTTP:URL=https://doi.org/10.1007/0-387-33416-5 |
| Subjects | LCSH:Operations research LCSH:Mathematical optimization LCSH:Mathematical models LCSH:Management science LCSH:Production management LCSH:Mathematics—Data processing FREE:Operations Research and Decision Theory FREE:Optimization FREE:Mathematical Modeling and Industrial Mathematics FREE:Operations Research, Management Science FREE:Operations Management FREE:Computational Mathematics and Numerical Analysis |
| Classification | LCC:T57.6-.97 DC23:658.403 |
| ID | 8000065000 |
| ISBN | 9780387334165 |
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