Study Multiobjective Optimization Using by Mousa Abd (8 results)
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Taschenbuch. Condition: Neu. Study on multiobjective optimization using improved genetic Algorithm | Methodology and Application | Abd Allah A. Mousa | Taschenbuch | 144 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846548899 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 No…rderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Paperback. Condition: Brand New. 144 pages. 8.66x5.91x0.33 inches. In Stock.
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Paperback. Condition: Brand New. 144 pages. 8.66x5.91x0.33 inches. In Stock.
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paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Language: English
Published by LAP LAMBERT Academic Publishing Nov 2011 2011
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Taschenbuch. Condition: 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.
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Condition: 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, Facult…y of Engineering, Menoufia Univ.
Language: English
Published by LAP LAMBERT Academic Publishing Nov 2011 2011
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 com…promise 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 144 pp. Englisch.
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Taschenbuch. Condition: 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 comp…romise 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.
