Reactive Search and Intelligent Optimization

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 Optim...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Battiti, Roberto (Autor), Brunato, Mauro (Autor), Mascia, Franco (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer US : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Colección:Operations Research/Computer Science Interfaces Series, 45
Temas:
Operations research.
Management science.
Artificial intelligence.
Engineering mathematics.
Engineering-Data processing.
Industrial engineering.
Production engineering.
Operations Research, Management Science .
Operations Research and Decision Theory.
Artificial Intelligence.
Mathematical and Computational Engineering Applications.
Industrial and Production Engineering.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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