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Evolutionary Algorithms (Computer Engineering: Metaheuristic
1,165.00 EGP

Evolutionary Algorithms (Computer Engineering: Metaheuristic

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1,165.00 EGP 

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Category Type
Computer Sciences
ISBN
9781848218048
Author
Petrowski, A.
Publisher
Wiley-ISTE.
Description:

  • Evolutionary algorithms are bio-inspired algorithms based on Darwin s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary ...

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PRODUCT INFORMATION

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    Specifications

    Category Type
    Computer Sciences
    ISBN
    9781848218048
    Item EAN
    2724602710645
    People
    Author
    Petrowski, A.
    People
    Publisher
    Wiley-ISTE.
    Category Type
    Computer Sciences
    ISBN
    9781848218048
    Item EAN
    2724602710645
    People
    Author
    Petrowski, A.
    People
    Publisher
    Wiley-ISTE.
    Technical Information
    Binding
    Paperback
    Languages and countries
    Book Language
    English
    Read more
  •  

    Description:

    • Evolutionary algorithms are bio-inspired algorithms based on Darwin s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to
    • Evolutionary algorithms are bio-inspired algorithms based on Darwin s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.
 

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