Reactive Search and Intelligent Optimization

• Main Authors: Battiti, Roberto (Author), Brunato, Mauro (Author), Mascia, Franco (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Boston, MA : Springer US, 2009.
• Series: Operations Research/Computer Science Interfaces Series, 45
• Subjects: Mathematics.; Operations research.; Decision making.; Computers.; Artificial intelligence.; Management science.; Applied mathematics.; Engineering mathematics.; Industrial engineering.; Production engineering.; Operations Research, Management Science.; Operation Research/Decision Theory.; Computing Methodologies.; Artificial Intelligence (incl. Robotics).; Appl.Mathematics/Computational Methods of Engineering.; Industrial and Production Engineering.

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...

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.