04127nam a22005415i 4500 978-0-387-09624-7 DE-He213 20260521091820.0 cr nn 008mamaa 110401s2009 xxu| s |||| 0|eng d 9780387096247 99780387096247 10.1007/978-0-387-09624-7 doi CICY Battiti, Roberto. author. Reactive Search and Intelligent Optimization [recurso electrónico] / by Roberto Battiti, Mauro Brunato, Franco Mascia. Boston, MA : Springer US, 2009. X, 182p. 74 illus. online resource. text txt rdacontent computer c rdamedia recurso en línea cr rdacarrier text file PDF rda Operations Research/Computer Science Interfaces Series, 1387-666X ; 45 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. MATHEMATICS. ELECTRONIC DATA PROCESSING. ARTIFICIAL INTELLIGENCE. OPERATIONS RESEARCH. ENGINEERING MATHEMATICS. INDUSTRIAL ENGINEERING. MATHEMATICS. OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING. OPERATIONS RESEARCH/DECISION THEORY. COMPUTING METHODOLOGIES. ARTIFICIAL INTELLIGENCE (INCL. ROBOTICS). APPL.MATHEMATICS/COMPUTATIONAL METHODS OF ENGINEERING. INDUSTRIAL AND PRODUCTION ENGINEERING. Brunato, Mauro. author. Mascia, Franco. author. SpringerLink (Online service) Springer eBooks Printed edition: 9780387096230 Operations Research/Computer Science Interfaces Series, 1387-666X ; 45 http://dx.doi.org/10.1007/978-0-387-09624-7 Ver el texto completo en las instalaciones del CICY ddc ER 31934 31934 0 0 ddc 0 0 LE CICY CICY EL 2025-07-10 0 2025-07-10 08:39:24 2025-07-10 ER