Details

Reactive Search and Intelligent Optimization


Reactive Search and Intelligent Optimization


Operations Research/Computer Science Interfaces Series, Band 45

von: Roberto Battiti, Mauro Brunato, Franco Mascia

118,99 €

Verlag: Springer
Format: PDF
Veröffentl.: 16.12.2008
ISBN/EAN: 9780387096247
Sprache: englisch
Anzahl Seiten: 196

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Beschreibungen

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 for the automated tuning of these parameters.
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.
Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. By automatically adjusting the working parameters, a reactive search self-tunes and adapts, effectively learning by doing until a solution is found. Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models, experimental algorithms applied to optimization, intelligent tuning and design of heuristics.
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 and schemes for the automated tuning of these parameters. Anyone working in decision making in business, engineering, economics or science will find a wealth of information here.
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.
Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems.  By automatically adjusting the working parameters, a reactive search self-tunes and adapts, effectively learning by doing until a solution is found.  Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models, experimental algorithms applied to optimization, intelligent tuning and design of heuristics.
 
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 and schemes for the automated tuning of these parameters.  Anyone working in decision making in business, engineering, economics or science will find a wealth of information here.
 
Comprehensive introduction to principles of reactive search
Focuses on methods, but uses problems to help demonstrate points
Important reading for anyone in decision science within areas of business, engineering, economics, or science

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