Metaheuristic Procedures for Training Neutral Networks

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Alba, Enrique (Επιμελητής έκδοσης), Martí, Rafael (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2006.
• Σειρά: Operations Research/Computer Science Interfaces Series, 36
• Θέματα: Business.; Production management.; Operations research.; Decision making.; Computer mathematics.; Mathematical models.; Mathematical optimization.; Management science.; Business and Management.; Operation Research/Decision Theory.; Optimization.; Mathematical Modeling and Industrial Mathematics.; Operations Research, Management Science.; Operations Management.; Computational Mathematics and Numerical Analysis.

 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. 

 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.