ISBN: 9780195099713 - evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms (23 resultados)

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Hardcover. Condición: As New. 1st Edition. Oxford University Press hardcover sans dust jacket (as issued, 1996-first edition/1st printing; unused and carefully stored; No marks/stains/stickers or other defects; (as New). We will add a custom fitted archival quality mylar cover, bubble wrap the book and ship it in a New BOX- Not a plastic bag like the zombie sellers.…

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Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

Condición: New. Compares three prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies and evolutionary programming - computational methods at the border between computer science and evolutionary biology. The algorithms are explained within a common framework, thereby clarifying the similarities and differences. Num Pages: 328 pages, line figures, tables. BIC Classification: PSAJ; UM; UYA. Category: (U) Tertiary Education (US: College). Dimension: 241 x 163 x 23. Weight in Grams: 632. . 1996. Hardback. . . . . Books ship from the US and Ireland. …

Condición: New. Compares three prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies and evolutionary programming - computational methods at the border between computer science and evolutionary biology. The algorithms are explained within a common framework, thereby clarifying the similarities and differences. Num Pages: 328 pages, line figures, tables. BIC Classification: PSAJ; UM; UYA. Category: (U) Tertiary Education (US: College). Dimension: 241 x 163 x 23. Weight in Grams: 632. . 1996. Hardback. . . . . …

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Hardcover. Condición: new. Hardcover. 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 author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presentedwithin a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents 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 further topics 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 handlingmixed integer 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 incomputer science and engineering disciplines, as well as graduate students in these fields. A comparison of the three most prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies and evolutionary programming - computational methods at the border between computer science and evolutionary biology. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condición: new. Hardcover. 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 author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presentedwithin a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents 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 further topics 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 handlingmixed integer 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 incomputer science and engineering disciplines, as well as graduate students in these fields. A comparison of the three most prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies and evolutionary programming - computational methods at the border between computer science and evolutionary biology. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Hardcover. Condición: new. Hardcover. 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 author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presentedwithin a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents 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 further topics 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 handlingmixed integer 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 incomputer science and engineering disciplines, as well as graduate students in these fields. A comparison of the three most prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies and evolutionary programming - computational methods at the border between computer science and evolutionary biology. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 author compares 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 also presents 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 further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithmstrategies and genetic algorithms to yield a hybrid capable of handling mixed integer 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.…

Condición: New. Print on Demand pp. 332.

Condición: New. Print on Demand pp. 332 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 332.