Handbook of Metaheuristics (International Series in Operations Research & Management Science)

ISBN: 1402072635

Format: pdf

Category: Uncategorized


Posted on 2007-09-12, updated at 2008-02-18. By anonymous.

Description



Fred W. Glover (Editor), Gary A. Kochenberger (Editor), "Handbook of Metaheuristics"
Pages: 570 | Publisher: Springer; 1 edition (January 1, 2003) | ISBN: 1402072635 | English | PDF | 8.8 MB

"The ‘Handbook of Metaheuristics’ consists of 19 chapters each describing one metaheuristic. Every chapter is authored by one or more experts in the individual field … . I am satisfied that their individual contents are of high quality. … So, in short, an excellent book if you want to learn about a number of individual metaheuristics."
(U Aickelin, Journal of the Operational Research Society, Issue 56, 2005)

The Handbook of Metaheuristics provides both the research and practitioner communities with a comprehensive coverage of the metaheuristic methodologies that have proven to be successful in a wide variety of real-world problem settings. Moreover, it is these metaheuristic strategies that hold particular promise for success in the future. The various chapters serve as stand alone presentations giving both the necessary background underpinnings as well as practical guides for implementation. In most settings a problem solver has an option as to which metaheuristic approach should be adopted for the problem at hand. Alternative methodologies typically exist that could be employed to produce high quality solutions. Often it becomes a matter of choosing one of several approaches that could be adopted. The very nature of metaheuristics invites an analyst to modify basic methods in response to problem characteristics, past experiences, and personal preferences. The chapters in this handbook are designed to facilitate this as well. This Handbook consists of 19 chapters. Topics covered include Scatter Search, Tabu Search, Genetic Algorithms, Genetic Programming, Memetic Algorithms, Variable Neighborhood Search, Guided Local Search, GRASP, Ant Colony Optimization, Simulated Annealing, Iterated Local Search, Multi-Start Methods, Constraint Programming, Constraint Satisfaction, Neural Network Methods for Optimization, Hyper-Heuristics, Parallel Strategies for Metaheuristics, Metaheuristic Class Libraries, and A-Teams. This family of metaheuristic chapters provides a state-of-the-art, comprehensive coverage of the major topics and methodologies of modern metaheuristics.
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