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Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Sprache: Englisch
Verlag: Nova Science Publishers Inc, 2015
ISBN 10: 1634634934 ISBN 13: 9781634634939
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HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Sprache: Englisch
Verlag: Nova Science Publishers Inc, 2015
ISBN 10: 1634634934 ISBN 13: 9781634634939
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In den WarenkorbHRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Sprache: Englisch
Verlag: Nova Science Publishers, Incorporated, 2015
ISBN 10: 1634634934 ISBN 13: 9781634634939
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Sprache: Englisch
Verlag: Nova Science Publishers, Incorporated, 2015
ISBN 10: 1634634934 ISBN 13: 9781634634939
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Sprache: Englisch
Verlag: Nova Science Publishers, Inc, 2015
ISBN 10: 1634634934 ISBN 13: 9781634634939
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Gebunden. Zustand: New. KlappentextrnrnA common approach for solving simulation-driven engineering problems is by using metamodel-assisted optimization algorithms, namely, in which a metamodel approximates the computationally expensive simulation and provides predicted.
Sprache: Englisch
Verlag: Nova Science Publishers Inc, 2015
ISBN 10: 1634634934 ISBN 13: 9781634634939
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Zustand: New. Editor(s): Washington, Sean. Num Pages: 157 pages. BIC Classification: UY. Category: (P) Professional & Vocational. Dimension: 240 x 164 x 19. Weight in Grams: 474. . 2015. UK ed. Hardcover. . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Nova Science Publishers Inc Jan 2015, 2015
ISBN 10: 1634634934 ISBN 13: 9781634634939
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Neuware - A common approach for solving simulation-driven engineering problems is by using metamodel-assisted optimization algorithms, namely, in which a metamodel approximates the computationally expensive simulation and provides predicted values at a lower computational cost. Such algorithms typically generate an initial sample of solutions which are then used to train a preliminary metamodel and to initiate an optimization process. One approach for generating the initial sample is with the design of experiment methods which are statistically oriented, while the more recent search-driven sampling approach invokes a computational intelligence optimizer such as an evolutionary algorithm, and then uses the vectors it generated as the initial sample. This book discusses research and new developments on evolutionary computation.