Metaheuristics

Karl F. Doerner

ISBN 10: 0387719199 / ISBN 13: 9780387719191

Edité par Springer US Aug 2007, 2007

A propos de cet article

This item is printed on demand - it takes 3-4 days longer - Neuware -This book's aim is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field. 424 pp. Englisch.

N° de réf. du vendeur 9780387719191

Détails bibliographiques

• Titre: Metaheuristics
• Éditeur: Springer US Aug 2007
• Date de parution: 2007
• Langue: anglais
• ISBN à 10 chiffres: 0387719199
• ISBN à 13 chiffres: 9780387719191
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 209 L x 132 H x 28 P (en millimètres) 209 millimètres de largeur par 132 millimètres de hauteur par 28 millimètres de profondeur
• Poids: 546 grammes

A propos de ce titre

Synopsis

The aim of Metaheuristics: Progress in Complex Systems Optimization is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field.

Highlighted are recent developments in the areas of Simulated Annealing, Path Relinking, Scatter Search, Tabu Search, Variable Neighborhood Search, Hyper-heuristics, Constraint Programming, Iterated Local Search, GRASP, bio-inspired algorithms like Genetic Algorithms, Memetic Algorithms, Ant Colony Optimization or Swarm Intelligence, and several other paradigms.