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Synopsis
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.
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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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Evolutionary Multiobjective Optimization with Gaussian Process Models
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LAP LAMBERT Academic Publishing Jul 2015, 2015
ISBN 10: 365975935X
ISBN 13: 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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Evolutionary Multiobjective Optimization with Gaussian Process Models
Published by
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 365975935X
ISBN 13: 9783659759352
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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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ISBN 10: 365975935X
ISBN 13: 9783659759352
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Evolutionary Multiobjective Optimization with Gaussian Process Models
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LAP LAMBERT Academic Publishing Jul 2015, 2015
ISBN 10: 365975935X
ISBN 13: 9783659759352
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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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Evolutionary Multiobjective Optimization with Gaussian Process Models
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ISBN 10: 365975935X
ISBN 13: 9783659759352
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