Battiti, Roberto. creator author. Brunato, Mauro. author. Mascia, Franco. author. SpringerLink (Online service) text xxu 2009 monographic eng

electronic

recurso electrónico

access X, 182p. 74 illus. online resource. 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. by Roberto Battiti, Mauro Brunato, Franco Mascia. 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 9780387096247 99780387096247 http://dx.doi.org/10.1007/978-0-387-09624-7 http://dx.doi.org/10.1007/978-0-387-09624-7 110401 20260521091820.0 978-0-387-09624-7