Roberto Battiti, Mauro Brunato, Franco Mascia

Reactive Search and Intelligent Optimization

• Gebundenes Buch

Produktbeschreibung

This book presents the main principles of reactive search and intelligent optimization and clearly shows how they can be used in problem solving. The book will be of interest to anyone working in the meta-heuristics.

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Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics.

Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more. Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities forthe automated tuning of these parameters.

Produktdetails

• Operations Research/Computer Science Interfaces Series 45
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 12198153, 978-0-387-09623-0
• 1st Edition.2nd Printing. 2008
• Seitenzahl: 196
• Erscheinungstermin: 6. November 2008
• Englisch
• Abmessung: 244mm x 161mm x 16mm
• Gewicht: 424g
• ISBN-13: 9780387096230
• ISBN-10: 038709623X
• Artikelnr.: 23875498

Inhaltsangabe

Preface.- Introduction.- Reacting on the neighborhood.- Reacting on the annealing schedule.- Reactive prohibitions.- Model-based search.- Reacting on the objective function.- Reinforcement learning.- Algorithm portfolios and restart strategies.- Racing.- Metrics, landscapes, and features.- Relationships between reactive search and reinforcement learning.- Index.

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Introduction: Machine Learning for Intelligent Optimization.- Reacting on the neighborhood.- Reacting on the Annealing Schedule.- Reactive Prohibitions.- Reacting on the Objective Function.- Reacting on the Objective Function.- Supervised Learning.- Reinforcement Learning.- Algorithm Portfolios and Restart Strategies.- Racing.- Teams of Interacting Solvers.- Metrics, Landscapes and Features.- Open Problems.