Autonomous Search
Language: English
Published by Springer, 2012
- Hardcover
- Used
Seller: killarneybooks, Inagh, Clare, Irelandkillarneybooks
AbeBooks seller since April 20, 2017
Condition: Used - Good
£ 30.81
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Add to basketItem description from seller
Hardcover, xv + 305 pages, NOT ex-library. Front blank endpaper stuck to the front board; neatly removed title page. Book is clean and bright with unmarked text, free of inscriptions and stamps, firmly bound. Issued without a dust jacket. -- Contents: 1 An Introduction to Autonomous Search [What Is an Autonomous Solver? (Architecture & Configuration of the Solver); Outline and Overview of the Book; Guideline for Readers]; Part I Off-line Configuration 2 Evolutionary Algorithm Parameters and Methods to Tune Them [Background and Objectives; Evolutionary Algorithms, Parameters, Algorithm Instances; Algorithm Design and Parameter Tuning; Utility, Algorithm Performance, Test Functions; Algorithmic Approaches to Parameter Tuning; Successful Case Studies on Tuning Evolutionary Algorithms; Considerations for Tuning EAs; Conclusions and Outlook] 3 Automated Algorithm Configuration and Parameter lining [Racing Procedures ; ParamlLS; Sequential Model-Based Optimisation; Other Approaches; Conclusions and Future Work] 4 Case-Based Reasoning for Autonomous Constraint Solving [Case-Based Reasoning; Case-Based Reasoning and Search; CPHYDRA: A Case-Based Portfolio Constraint Solver; Concluding Remarks] 5 Learning a Mixture of Search Heuristics [Machine Learning and Mixtures of Experts; Constraint Satisfaction and Heuristic Search; Search with More than One Heuristic; ACE; Techniques that Improve Learning; Results; Conclusions and Future Work]; Part II On-line Control 6 An Investigation of Reinforcement Learning for Reactive Search Optimization [Reinforcement Learning for Optimization; Reinforcement Learning and Dynamic Programming Basics; Reactive SAT/MAX-SAT Solvers; RL-Based Approach for Reactive SAT/MAX-SAT Solvers; Experimental Results] 7 Adaptive Operator Selection and Management in Evolutionary Algorithms [Parameter Setting in Evolutionary Algorithms; Adaptive Operator Selection; Adaptive Operator Management] 8 Parameter Adaptation in Ant Colony Optimization [Ant Colony Optimization; Overview of Parameter Adaptation Approaches; Parameter Adaptation in ACO; Experimental Investigation of Fixed Parameter Settings; Prescheduled Parameter Variation for MMAS]; Part III New Directions and Applications 9 Continuous Search in Constraint Programming [Background and Notations; Continuous Search in Constraint Programming; Dynamic Continuous Search; Experimental Validation; Related Works; Discussion and Perspectives] 10 Control-Based Clause Sharing in Parallel SAT Solving [Introduction; Previous Works; Technical Background; Control-Based Clause Sharing in Parallel SAT Solving; Evaluation; Conclusion] 11 Learning Feature-Based Heuristic Functions [Introduction; Search Framework; Learning Heuristic Functions; Feature Combination as a Linear Program; Approximation Bounds; Empirical Results; Conclusion; References]. …
Seller Inventory # 006535
- Title
- Autonomous Search
- Author
- Youssef Hamadi; Eric Monfroy; Frederic Saubion
- Publisher
- Springer
- Publication year
- 2012
- Condition
- Good
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3642214339
- ISBN 13
- 9783642214332
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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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