Verlag: LAP LAMBERT Academic Publishing Nov 2011, 2011
ISBN 10: 3846548898 ISBN 13: 9783846548899
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware -Many real-world problems involve two types of problem difficulty: I) multiple, conflicting objectives and II) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Pareto optimal solutions concurrently in a single simulation run. The subject of this work is the improvement of multiobjective evolutionary algorithms and their application to engineering problems.Books on Demand GmbH, Überseering 33, 22297 Hamburg 144 pp. Englisch.
Verlag: Lap Lambert Academic Publishing, 2011
ISBN 10: 3846548898 ISBN 13: 9783846548899
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: Brand New. 144 pages. 8.66x5.91x0.33 inches. In Stock.
Verlag: Lap Lambert Academic Publishing, 2011
ISBN 10: 3846548898 ISBN 13: 9783846548899
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: Brand New. 144 pages. 8.66x5.91x0.33 inches. In Stock.
Verlag: LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846548898 ISBN 13: 9783846548899
Sprache: Englisch
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In den Warenkorbpaperback. Zustand: New. New. book.
Verlag: LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846548898 ISBN 13: 9783846548899
Sprache: Englisch
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mousa Abd Allah A.A. A. Mosua obtained his PhD in in engineering mathematics from faculty of engineering, Menoufia University, Egypt. He is currently a Lecturer in the Basic Engineering Sciences, Faculty of Engineering, Menoufia Univ.
Verlag: LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846548898 ISBN 13: 9783846548899
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many real-world problems involve two types of problem difficulty: I) multiple, conflicting objectives and II) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Pareto optimal solutions concurrently in a single simulation run. The subject of this work is the improvement of multiobjective evolutionary algorithms and their application to engineering problems.
Verlag: LAP LAMBERT Academic Publishing Nov 2011, 2011
ISBN 10: 3846548898 ISBN 13: 9783846548899
Sprache: Englisch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
EUR 59,00
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In den WarenkorbTaschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many real-world problems involve two types of problem difficulty: I) multiple, conflicting objectives and II) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Pareto optimal solutions concurrently in a single simulation run. The subject of this work is the improvement of multiobjective evolutionary algorithms and their application to engineering problems. 144 pp. Englisch.