Miha Mlakar (7 results)
- Softcover
Seller: moluna, Greven, Germanymoluna
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- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Evolutionary Multiobjective Optimization with Gaussian Process Models | Miha Mlakar | Taschenbuch | 116 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659759352 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbie…ter: preigu.
- Softcover
Seller: Mispah books, Redhill, SURRE, United KingdomMispah books
Contact seller4-star sellerCondition: Used - As new
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paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Condition: New. Heute sind diese Leute anders gruppiert, doch ihre Verachtung ist noch immer dieselbe, weil sie so verdammt normal sein muessen. Weil es sich lohnt, weil es sich immer gelohnt hat. Auch wenn niemand mehr sagen kann, was normal ist. Wir wissen aber immer no.
Language: English
Published by LAP LAMBERT Academic Publishing Jul 2015, 2015
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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£ 48.30
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on the field of surrogate-model-based multiobjective evolutionary optimization. It describes the sate-of-the-art concepts and methods, presents various optimization problems and describes current challenges. The ma…in contributions are done for the optimization problems, where solutions are presented with uncertainty. To compare solutions under uncertainty and improve the optimization results the new relations for comparing solutions under uncertainty are defined. These relations reduce the possibility of incorrect comparisons due to the inaccurate approximations. The relations under uncertainty are then used in the new surrogate-model-based multiobjective evolutionary algorithm called GP-DEMO. The algorithm is thoroughly tested on benchmark and real-world problems and the results show that GP-DEMO, in comparison to other multiobjective evolutionary algorithms, produces comparable results while requiring fewer exact evaluations of the original objective functions. 116 pp. Englisch.
Language: English
Published by LAP LAMBERT Academic Publishing Jul 2015, 2015
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book focuses on the field of surrogate-model-based multiobjective evolutionary optimization. It describes the sate-of-the-art concepts and methods, presents various optimization problems and describes current challenges. The main c…ontributions are done for the optimization problems, where solutions are presented with uncertainty. To compare solutions under uncertainty and improve the optimization results the new relations for comparing solutions under uncertainty are defined. These relations reduce the possibility of incorrect comparisons due to the inaccurate approximations. The relations under uncertainty are then used in the new surrogate-model-based multiobjective evolutionary algorithm called GP-DEMO. The algorithm is thoroughly tested on benchmark and real-world problems and the results show that GP-DEMO, in comparison to other multiobjective evolutionary algorithms, produces comparable results while requiring fewer exact evaluations of the original objective functions.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch.
- Softcover
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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£ 48.88
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book focuses on the field of surrogate-model-based multiobjective evolutionary optimization. It describes the sate-of-the-art concepts and methods, presents various optimization problems and describes current challenges. The main co…ntributions are done for the optimization problems, where solutions are presented with uncertainty. To compare solutions under uncertainty and improve the optimization results the new relations for comparing solutions under uncertainty are defined. These relations reduce the possibility of incorrect comparisons due to the inaccurate approximations. The relations under uncertainty are then used in the new surrogate-model-based multiobjective evolutionary algorithm called GP-DEMO. The algorithm is thoroughly tested on benchmark and real-world problems and the results show that GP-DEMO, in comparison to other multiobjective evolutionary algorithms, produces comparable results while requiring fewer exact evaluations of the original objective functions.
