FREE delivery to all EXCLUSIVE BOOKS stores nationwide. FREE delivery to your door on all orders over R450. Excludes all international deliveries.

Evolutionary Optimization Algorithms

D Simon
    Product form
      FORMAT: Hardback

      R 4,585.00 Price and availability exclusive to website

      YOU COULD EARN 4,585 FUTURE RETAIL DISCOUNTS.
      ESTIMATED DELIVERY: Approx. 20 - 30 Business Days
      BUY NOW PAY LATER
      From R 764.16 per month!
      3x monthly payments of R 1,528.33 with
      4x fortnightly payments of R 1,146.25 with
      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 implementationGives 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 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.
      Format: CONTRIBUTORS: D Simon EAN: 9780470937419 COUNTRY: United States PAGES: WEIGHT: 1225 g HEIGHT: 239 cm
      PUBLISHED BY: John Wiley & Sons Inc DATE PUBLISHED: 2013-05-17 CITY: GENRE: MATHEMATICS / Discrete Mathematics WIDTH: 160 cm SPINE:

      Book Themes:

      Discrete mathematics

      Customer Reviews

      Be the first to write a review
      0%
      (0)
      0%
      (0)
      0%
      (0)
      0%
      (0)
      0%
      (0)
      DAN SIMON is a Professor at Cleveland State University in the Department of Electrical and Computer Engineering. His teaching and research interests include control theory, computer intelligence, embedded systems, technical writing, and related subjects. He is the author of the book Optimal State Estimation (Wiley).
      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 implementationGives 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 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.
      Format: CONTRIBUTORS: D Simon EAN: 9780470937419 COUNTRY: United States PAGES: WEIGHT: 1225 g HEIGHT: 239 cm
      PUBLISHED BY: John Wiley & Sons Inc DATE PUBLISHED: 2013-05-17 CITY: GENRE: MATHEMATICS / Discrete Mathematics WIDTH: 160 cm SPINE:

      Book Themes:

      Discrete mathematics

      Customer Reviews

      Be the first to write a review
      0%
      (0)
      0%
      (0)
      0%
      (0)
      0%
      (0)
      0%
      (0)
      DAN SIMON is a Professor at Cleveland State University in the Department of Electrical and Computer Engineering. His teaching and research interests include control theory, computer intelligence, embedded systems, technical writing, and related subjects. He is the author of the book Optimal State Estimation (Wiley).

      Recently viewed products

      Login

      Forgot your password?

      Don't have an account yet?
      Create account