Metaheuristics Progress in Complex Systems Optimization /

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 co...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Doerner, Karl F. (Editor ), Gendreau, Michel (Editor ), Greistorfer, Peter (Editor ), Gutjahr, Walter (Editor ), Hartl, Richard F. (Editor ), Reimann, Marc (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer US : Imprint: Springer, 2007.
Edición:1st ed. 2007.
Colección:Operations Research/Computer Science Interfaces Series, 39
Temas:
Mathematical optimization.
Operations research.
Management science.
Artificial intelligence.
Mathematical models.
Computer science.
Optimization.
Operations Research and Decision Theory.
Operations Research, Management Science .
Artificial Intelligence.
Mathematical Modeling and Industrial Mathematics.
Theory of Computation.
Acceso en línea:Texto Completo
Descripción
Sumario: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.
Descripción Física:XIV, 410 p. 66 illus. online resource.
ISBN:9780387719214
ISSN:2698-5489 ;

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