Metaheuristics: From Design to Implementation: 74 (Wiley Series on Parallel and Distributed Computing). Dieser Artikel ist nicht verfügbar.
Sprache: Englisch
Verlag: John Wiley & Sons 2009-06-04, 2009
- Hardcover
- Neu

Anbieter: Chiron Media, Wallingford, Vereinigtes KönigreichChiron Media
Verkäufer:in bei AbeBooks seit 2. August 2010
Zustand: Neu
EUR 152,70
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Bestandsnummer des Verkäufers 6666-GRD-9780470278581
- Titel
- Metaheuristics: From Design to Implementation: 74 (Wiley Series on Parallel and Distributed Computing)
- Autor
- Talbi
- Verlag
- John Wiley & Sons 2009-06-04
- Erscheinungsjahr
- 2009
- Zustand
- New
- Einband
- Hardcover
- Sprache
- Englisch
- ISBN-10
- 0470278587
- ISBN-13
- 9780470278581
This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code.
Throughout the book, the key search components of metaheuristics are considered as a toolbox for:
- Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems
-
Designing efficient metaheuristics for multi-objective optimization problems
-
Designing hybrid, parallel, and distributed metaheuristics
-
Implementing metaheuristics on sequential and parallel machines
Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.
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