Buch, Englisch, 196 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 324 g
Reihe: Operations Research/Computer Science Interfaces Series
Buch, Englisch, 196 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 324 g
Reihe: Operations Research/Computer Science Interfaces Series
ISBN: 978-1-4419-3499-4
Verlag: Springer US
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik Mathematik Operations Research
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
Weitere Infos & Material
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.
