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Published by Springer, 2008
ISBN 10: 3540926941ISBN 13: 9783540926948
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ISBN 10: 3540926941ISBN 13: 9783540926948
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ISBN 10: 3540926941ISBN 13: 9783540926948
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Published by Springer, 2022
ISBN 10: 3030702790ISBN 13: 9783030702793
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Published by Springer Berlin Heidelberg, 2008
ISBN 10: 3540926941ISBN 13: 9783540926948
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Condition: Sehr gut. Zustand: Sehr gut - Neubindung, Buchrücken leicht geknickt, Aufl. 2008 | Seiten: 256 | Sprache: Englisch.
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ISBN 10: 3030702790ISBN 13: 9783030702793
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ISBN 10: 3540926941ISBN 13: 9783540926948
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8-12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is 'in the loop' as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of 'learning' about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can - prove the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained. 256 pp. Englisch.
Published by Springer, 2008
ISBN 10: 3540926941ISBN 13: 9783540926948
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ISBN 10: 3540926941ISBN 13: 9783540926948
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ISBN 10: 3030702790ISBN 13: 9783030702793
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Published by Springer 2022-04, 2022
ISBN 10: 3030702790ISBN 13: 9783030702793
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Published by Springer Berlin Heidelberg, 2008
ISBN 10: 3540926941ISBN 13: 9783540926948
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8-12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is 'in the loop' as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of 'learning' about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can - prove the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.
Published by Springer, 2021
ISBN 10: 3030702766ISBN 13: 9783030702762
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Published by Springer, 2022
ISBN 10: 3030702790ISBN 13: 9783030702793
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Published by Springer, 2021
ISBN 10: 3030702766ISBN 13: 9783030702762
Seller: Basi6 International, Irving, TX, U.S.A.
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Published by Springer 2008-12, 2008
ISBN 10: 3540926941ISBN 13: 9783540926948
Seller: Chiron Media, Wallingford, United Kingdom
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