evolutionary optimization algorithms von simon dan (18 Ergebnisse)

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Zustand: New. pp. 772.

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Zustand: New. This book is a clear and lucid presentation of Evolutionary Algorithms, with a straightforward, bottom-up approach that provides the reader with a firm grasp of the basic principles of EAs. Num Pages: 772 pages, Illustrations. BIC Classification: PBD. Category: (P) Professional & Vocational. Dimension: 241 x 162 x 44. Weight in Grams: 1236. . 2013. 1st Edition. Hardcover. . . . .

Zustand: New. pp. 772.

Hardcover. Zustand: Brand New. 1st edition. 772 pages. 9.50x6.50x1.75 inches. In Stock.

Zustand: New. This book is a clear and lucid presentation of Evolutionary Algorithms, with a straightforward, bottom-up approach that provides the reader with a firm grasp of the basic principles of EAs. Num Pages: 772 pages, Illustrations. BIC Classification: PBD. Category: (P) Professional & Vocational. Dimension: 241 x 162 x 44. Weight in Grams: 1236. . 2013. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - A clear and lucid bottom-up approach to the basic principles of evolutionary algorithmsEvolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies.This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others.Evolutionary Optimization Algorithms:\* Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear-but theoretically rigorous-understanding of evolutionary algorithms, with an emphasis on implementation\* Gives a careful treatment of recently developed EAs-including opposition-based learning, artificial fish swarms, bacterial foraging, and many others- and discusses their similarities and differences from more well-established EAs\* Includes chapter-end problems plus a solutions manual available online for instructors\* Offers simple examples that provide the reader with an intuitive understanding of the theory\* Features source code for the examples available on the author's website\* Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modelingEvolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

Hardcover. Zustand: new. Hardcover. A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clearbut theoretically rigorousunderstanding of evolutionary algorithms, with an emphasis on implementationGives a careful treatment of recently developed EAsincluding opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAsIncludes chapter-end problems plus a solutions manual available online for instructorsOffers simple examples that provide the reader with an intuitive understanding of the theoryFeatures source code for the examples available on the author's websiteProvides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science. This book is a clear and lucid presentation of Evolutionary Algorithms, with a straightforward, bottom-up approach that provides the reader with a firm grasp of the basic principles of EAs. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Zustand: Brand New. 1st edition. 772 pages. 9.50x6.50x1.75 inches. In Stock. This item is printed on demand.

Hardback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 1210.

Hardcover. Zustand: new. Hardcover. A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clearbut theoretically rigorousunderstanding of evolutionary algorithms, with an emphasis on implementationGives a careful treatment of recently developed EAsincluding opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAsIncludes chapter-end problems plus a solutions manual available online for instructorsOffers simple examples that provide the reader with an intuitive understanding of the theoryFeatures source code for the examples available on the author's websiteProvides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science. This book is a clear and lucid presentation of Evolutionary Algorithms, with a straightforward, bottom-up approach that provides the reader with a firm grasp of the basic principles of EAs. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.