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Evolutionary algorithms

By: Petrowski, Alain.
Contributor(s): Ben-Hamida, Sana [Co author].
Material type: materialTypeLabelBookSeries: Computer Engineering Series: Mataheuristics set: Volume 9. Publisher: London Wiley 2017Description: ix, 236 p. With index.ISBN: 9781848218048.Subject(s): Genetic algorithms | Probability & Statistics | MathematicsDDC classification: 519.3 Summary: 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. https://www.wiley.com/en-us/Evolutionary+Algorithms-p-9781848218048
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Non-fiction 519.3 P3E9 (Browse shelf) Checked out to Diptesh Ghosh (F20837) 23/02/2019 197836

Contents:
1. Evolutionary Algorithms.
2. Continuous Optimization.
3. Constrained Continuous
 Evolutionary Optimization.
4. Combinatorial Optimization.
5. Multi-objective Optimization.
6. Genetic Programming 
for Machine Learning

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.

https://www.wiley.com/en-us/Evolutionary+Algorithms-p-9781848218048

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