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In den WarenkorbHardcover. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
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EUR 63,54
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In den WarenkorbZustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
Verlag: Oxford , Oxford University Press, 1996
ISBN 10: 0195099710 ISBN 13: 9780195099713
Sprache: Englisch
Anbieter: Antiquariat Bookfarm, Löbnitz, Deutschland
EUR 69,30
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In den WarenkorbHardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 68 BAE 9780195099713 Sprache: Englisch Gewicht in Gramm: 1150.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 247,96
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In den WarenkorbZustand: New. In.
Verlag: Oxford University Press Jan 1996, 1996
ISBN 10: 0195099710 ISBN 13: 9780195099713
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 336,15
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In den WarenkorbBuch. Zustand: Neu. Neuware - This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the authorcompares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author alsopresents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are furthertopics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixedinteger optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.