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Evolutionary Optimization Algorithms: Biologically-Inspired and Population-Based Approaches to Computer Intelligence - Couverture rigide
Synopsis
Evolutionary Optimization Algorithms 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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Quatrième de couverture
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 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 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.
Présentation de l'éditeur
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. Covering the theory, history, mathematics, and applications of evolutionary optimization algorithms, this timely and practical book offers lengthy examples, a companion website, MATLAB code, and a Solutions Manual making it perfect for advanced undergraduates, graduates, and practicing engineers involved in engineering and computer science.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurJohn Wiley & Sons Inc
- Date d'édition2013
- ISBN 10 0470937416
- ISBN 13 9780470937419
- ReliureRelié
- Langueanglais
- Nombre de pages784
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Evolutionary Optimization Algorithms
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Evolutionary Optimization Algorithms : Biologically-Inspired and Population-Based Approaches to Computer Intelligence
Edité par
Wiley, 2013
ISBN 10 : 0470937416
ISBN 13 : 9780470937419
Ancien ou d'occasion
Couverture rigide
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Hardcover. Etat : 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9780470937419
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Evolutionary Optimization Algorithms : Biologically-Inspired and Population-Based Approaches to Computer Intelligence
Edité par
Wiley, 2013
ISBN 10 : 0470937416
ISBN 13 : 9780470937419
Ancien ou d'occasion
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ISBN 13 : 9780470937419
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