Reactive Search and Intelligent Optimization

Reactive Search and Intelligent Optimization by Roberto Battiti, Mauro Brunato, Franco Mascia.

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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Bibliographic Details
Main Authors: Battiti, Roberto (Author), Brunato, Mauro (Author), Mascia, Franco (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 2009.
Edition:1st ed. 2009.
Series:Operations Research/Computer Science Interfaces Series, 45
Springer eBook Collection.
Subjects:
Operations research.
Management science.
Decision making.
Artificial intelligence.
Applied mathematics.
Engineering mathematics.
Industrial engineering.
Production engineering.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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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