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In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume.There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected.Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.
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In den WarenkorbZustand: New. Includes contributions reporting theoretical developments in the design of neural networks and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of oil industrial processes, as well as to credit scoring, are reflected. Editor(s): Reeves, C. R.; Steele, N.C. Num Pages: 737 pages, 403 black & white illustrations, biography. BIC Classification: UYAM; UYQM; UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 279 x 210 x 38. Weight in Grams: 1832. . 1993. Paperback. . . . .
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In den WarenkorbZustand: New. pp. 756.
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In den WarenkorbZustand: New. Includes contributions reporting theoretical developments in the design of neural networks and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of oil industrial processes, as well as to credit scoring, are reflected. Editor(s): Reeves, C. R.; Steele, N.C. Num Pages: 737 pages, 403 black & white illustrations, biography. BIC Classification: UYAM; UYQM; UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 279 x 210 x 38. Weight in Grams: 1832. . 1993. Paperback. . . . . Books ship from the US and Ireland.
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Verlag: Springer Verlag GmbH, Vienna, 1993
ISBN 10: 3211824596 ISBN 13: 9783211824597
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. Artificial neural networks and genetic algorithms are both areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focusing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their teaming. Application areas include speech recognition, control of industrial processes, credit scoring, scheduling, design, combinatorial optimization, financial planning, times series, modeling and parallel implementations. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation.Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume. Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Verlag: Springer Verlag GmbH, Vienna, 1993
ISBN 10: 3211824596 ISBN 13: 9783211824597
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. Artificial neural networks and genetic algorithms are both areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focusing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their teaming. Application areas include speech recognition, control of industrial processes, credit scoring, scheduling, design, combinatorial optimization, financial planning, times series, modeling and parallel implementations. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation.Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume. Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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In den WarenkorbGebunden. Zustand: New. Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. Howe.
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Verlag: Springer-Verlag New York Inc., 2013
ISBN 10: 147577818X ISBN 13: 9781475778182
Sprache: Englisch
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In den WarenkorbZustand: New. Series: Operations Research/Computer Science Interfaces Series. Num Pages: 332 pages, 4 black & white illustrations, biography. BIC Classification: KJT; PBT; PBU; UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 18. Weight in Grams: 534. . 2013. Softcover reprint of the original 1st ed. 2002. Paperback. . . . .