Isbn: 9780470278581 - metaheuristics: from design to implementation (8 Ergebnisse)

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

Zustand: NEW.

Gebunden. Zustand: New. EL-GHAZALI TALBI is a full Professor in Computer Science at the University of Lille (France), and head of the optimization group of the Computer Science Laboratory (L.I.F.L.). His current research interests are in the fields of metaheuristics, parallel algo.

Hardcover. Zustand: Brand New. new edition. 593 pages. 9.50x6.50x1.25 inches. In Stock.

Zustand: New. Enables the readers to design and deploy powerful algorithms (methodologies and implementations) to solve complex optimization problems in a diverse range of industries. Includes numerous real-world examples to demonstrate how metaheuristics are applied Shows how to generate an efficient program from the design using the software framework. Num Pages: 624 pages, Illustrations. BIC Classification: PBU; UYA. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 166 x 243 x 319. Weight in Grams: 1102. . 2009. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.…

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 624 | Sprache: Englisch | Produktart: Bücher | 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.…

Buch. Zustand: Neu. Neuware - A unified view of metaheuristicsThis 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 machinesUsing 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.…