Evolutionary Algorithms Theory Practice by Bäck Thomas (16 results)

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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 328 | Sprache: Englisch | Produktart: Bücher | 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.

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Hardcover. Condition: 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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

Hardcover. Condition: 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.

Hardcover. Condition: 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.

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a unified view of evolutionaryalgorithms: the exciting new probabilistic search toolsinspired by biological models that have immense potential aspractical problem-solvers in a wide variety of settingsacademic, commercial, and industrial. In this work, theauthor compares the three most prominent representatives ofevolutionary algorithms: genetic algorithms, evolutionstrategies, and evolutionary programming. The algorithms arepresented within a unified framework, thereby clarifying thesimilarities and differences of these methods. The authoralso presents new results regarding the role of mutation andselection in genetic algorithms, showing how mutation seemsto be much more important for the performance of geneticalgorithms than usually assumed. The interaction ofselection and mutation, and the impact of the binary codeare further topics of interest. Some of the theoreticalresults are also confirmed by performing an experiment inmeta-evolution on a parallel computer. The meta-algorithmstrategies and genetic algorithms to yield a hybrid capableof handling mixed integer optimization problems. As adetailed description of the algorithms, with practicalguidelines for usage and implementation, this work willinterest a wide range of researchers in computer science andengineering disciplines, as well as graduate students inthese fields.