Evolutionary Algorithms-Volume 9, Alain Petrowski, Sana Ben-Hamida, 9781848218048, John Wiley, 2017, Hardcover
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In general, Petrowski and Ben-Hamid display an in-depth understanding of several optimization classes and their corresponding evolutionary algorithms, along with an impressive ability to explain, illustrate, motivate, classify and codify. Although nobody can "do it all" in a field as deep and wide as evolutionary computation, they have chosen a pertinent subset and done a fine job with it. My own copy of "Evolutionary Algorithms" became an instant go-to reference as I prepare for another semester of teaching. ( Genetic Programming and Evolvable Machines, December 2018)
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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Hardcover. Condición: new. Hardcover. Evolutionary algorithms are bio-inspired algorithms based on Darwins 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781848218048
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