Evolutionary Algorithms

Nonfiction, Computers, Programming
Cover of the book Evolutionary Algorithms by Alain Petrowski, Sana Ben-Hamida, Wiley
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Author: Alain Petrowski, Sana Ben-Hamida ISBN: 9781119136415
Publisher: Wiley Publication: April 11, 2017
Imprint: Wiley-ISTE Language: English
Author: Alain Petrowski, Sana Ben-Hamida
ISBN: 9781119136415
Publisher: Wiley
Publication: April 11, 2017
Imprint: Wiley-ISTE
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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