Limit this search to....

Metaheuristics
Contributor(s): Talbi, El-Ghazali (Author)
ISBN: 0470278587     ISBN-13: 9780470278581
Publisher: Wiley
OUR PRICE:   $153.85  
Product Type: Hardcover - Other Formats
Published: June 2009
Qty:
Annotation: "Metaheuristics" provides a complete background of metaheuristics, enabling readers to design and deploy powerful algorithms to solve complex optimization problems in a diverse range of industries. Using case studies in different domains, including telecommunications, transportation and logistics, bioinformatics, design engineering, and scheduling provides clear information for these diverse markets. The book is an effective resource for engineers, researchers, and developers, and an ideal text for graduate students in computer science, bioinformatics, electrical engineering, and applied mathematics courses.
Additional Information
BISAC Categories:
- Computers | Data Modeling & Design
- Mathematics | Probability & Statistics - General
Dewey: 519.6
LCCN: 2009017331
Series: Wiley Series on Parallel and Distributed Computing
Physical Information: 1.3" H x 6.3" W x 9.3" (2.10 lbs) 624 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
A unified view of metaheuristics

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.