Evolutionary Multiobjective Optimization with Gaussian Process Models
Miha Mlakar
ISBN 10: 365975935X ISBN 13: 9783659759352
Published by LAP LAMBERT Academic Publishing, 2015
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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 | Anbieter: preigu Print on Demand. Seller Inventory # 104275252
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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 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.
About the Author: Miha Mlakar finished his Ph.D. in Information and Communication Technologies from the Jožef Stefan International Postgraduate School in Ljubljana, Slovenia.He is currently working as a Postdoctoral Associate at Jožef Stefan Insitute, focusing on evolutionary algorithms, optimization, machine learning, data science and industrial applications.
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Title: Evolutionary Multiobjective Optimization ...
Publisher: LAP LAMBERT Academic Publishing
Publication Date: 2015
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Evolutionary Multiobjective Optimization with Gaussian Process Models
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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 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. Seller Inventory # 9783659759352
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Evolutionary Multiobjective Optimization with Gaussian Process Models
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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 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 116 pp. Englisch. Seller Inventory # 9783659759352
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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 main 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. Seller Inventory # 9783659759352
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