Overview
- The contributors are among the leading researchers in the areas of heuristics, optimization, evolutionary computing and constraints
- The book will be of benefit to researchers, engineers and postgraduates in the areas of constraint programming, machine learning and evolutionary computing
- This is the first book dedicated to this topic
- Includes supplementary material: sn.pub/extras
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Table of contents (11 chapters)
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Front Matter
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New Directions and Applications
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Front Matter
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Keywords
About this book
Decades of innovations in combinatorial problem solving have produced better and more complex algorithms. These new methods are better since they can solve larger problems and address new application domains. They are also more complex which means that they are hard to reproduce and often harder to fine-tune to the peculiarities of a given problem. This last point has created a paradox where efficient tools are out of reach of practitioners.
Autonomous search (AS) represents a new research field defined to precisely address the above challenge. Its major strength and originality consist in the fact that problem solvers can now perform self-improvement operations based on analysis of the performances of the solving process -- including short-term reactive reconfiguration and long-term improvement through self-analysis of the performance, offline tuning and online control, and adaptive control and supervised control. Autonomous search "crosses the chasm" and provides engineers and practitioners with systems that are able to autonomously self-tune their performance while effectively solving problems.
This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms.
Autonomous search (AS) represents a new research field defined to precisely address the above challenge. Its major strength and originality consist in the fact that problem solvers can now perform self-improvement operations based on analysis of the performances of the solving process -- including short-term reactive reconfiguration and long-term improvement through self-analysis of the performance, offline tuning and online control, and adaptive control and supervised control. Autonomous search "crosses the chasm" and provides engineers and practitioners with systems that are able to autonomously self-tune their performance while effectively solving problems.
This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms.
This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms.
This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms.
Editors and Affiliations
About the editors
Dr. Youssef Hamadi is the head of the Constraint Reasoning Group at Microsoft Research Cambridge, and his research interests include combinatorial optimization in alternative frameworks (parallel and distributed architectures); the application of machine learning to search; autonomous search; and parallel propositional satisfiability. Prof. Eric Monfroy is affiliated with both the Universidad Técnica Federico Santa María, Valparaíso, Chile and LINA, Université de Nantes, France; his research areas include heuristics, optimization, constraints, and search. Prof. Frédéric Saubion coheads the Metaheuristics, Optimization and Applications team at the Université d'Angers; his research topics include hybrid and adaptive evolutionary algorithms and applications of metaheuristics to various domains such as information retrieval, nonmonotonic reasoning and biology.
Bibliographic Information
Book Title: Autonomous Search
Editors: Youssef Hamadi, Eric Monfroy, Frédéric Saubion
DOI: https://doi.org/10.1007/978-3-642-21434-9
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-21433-2Published: 05 January 2012
Softcover ISBN: 978-3-642-44334-3Published: 22 February 2014
eBook ISBN: 978-3-642-21434-9Published: 05 January 2012
Edition Number: 1
Number of Pages: XVI, 308
Topics: Artificial Intelligence, Mathematics of Computing, Computational Intelligence, Theory of Computation, Control and Systems Theory