Self-Adaptive Heuristics for Evolutionary Computation, Oliver Kramer

Автор: Anne Auger, Benjamin Doerr
Название: Theory of randomized search heuristics
ISBN: 9814282669 ISBN-13(EAN): 9789814282666
Издательство: World Scientific Publishing
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Цена: 126720.00 T
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Описание: Randomized search heuristics such as evolutionary algorithms, genetic algorithms, evolution strategies, ant colony and particle swarm optimization turn out to be highly successful for optimization in practice. This book covers the theoretical developments in randomized search heuristics such as runtime analysis, drift analysis and convergence.

Автор: Thomas St?tzle
Название: Learning and Intelligent Optimization: Designing, Implementing and Analyzing Effective Heuristics
ISBN: 3642111688 ISBN-13(EAN): 9783642111686
Издательство: Springer
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Цена: 65210.00 T
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Описание: Third International Conference LION 2009 III Trento Italy January 1418 2009 Selected Papers. .

Автор: Giorgio Fasano
Название: Solving Non-standard Packing Problems by Global Optimization and Heuristics
ISBN: 3319050044 ISBN-13(EAN): 9783319050041
Издательство: Springer
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Цена: 46570.00 T
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Автор: Thomas St?tzle; Mauro Birattari; Holger H. Hoos
Название: Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
ISBN: 3540744452 ISBN-13(EAN): 9783540744450
Издательство: Springer
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Цена: 60550.00 T
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Описание: Constitutes the refereed proceedings of the International Workshop on Engineering Stochastic Local Search Algorithms 2007, held in Brussels, Belgium, September 6-8, 2007. This work covers topics including Methodological developments, behavior of SLS algorithms, search space analysis, algorithm performance, tuning procedures and AI/OR techniques.

Автор: Thomas St?tzle; Mauro Birattari; Holger H. Hoos
Название: Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
ISBN: 364203750X ISBN-13(EAN): 9783642037504
Издательство: Springer
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Описание: SLS methods include a wide spectrum of te- niques, ranging from constructive search procedures and iterative improvement algorithms to more complex SLS methods, such as ant colony optimization, evolutionary computation, iterated local search, memetic algorithms, simulated annealing, tabu search, and variable neighborhood search.