Reactive Search and Intelligent Optimization

• Main Authors: Battiti, Roberto. (Author), Brunato, Mauro. (Author), Mascia, Franco. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: Boston, MA : Springer US, 2009.
• Series: Operations Research/Computer Science Interfaces Series, 45
• Subjects: Mathematics.; Electronic data processing.; Artificial intelligence.; Operations research.; Engineering mathematics.; Industrial engineering.; Operations Research, Mathematical Programming.; Operations Research/Decision Theory.; Computing Methodologies.; Artificial Intelligence (incl. Robotics).; Appl.Mathematics/Computational Methods of Engineering.; Industrial and Production Engineering.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-0-387-09624-7

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:

• Preface
• Introduction
• Reacting on the neighborhood
• Reacting on the annealing schedule
• Reactive prohibitions
• Model-based search
• Reacting on the objective function
• Reinforcement learning
• Algorithm portfolios and restart strategies
• Racing
• Metrics, landscapes, and features
• Relationships between reactive search and reinforcement learning
• Index.