Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms: 186 (Studies in Computational Intelligence, 186) - Tapa dura

Goh, Chi-Keong; Tan, Kay Chen

 
9783540959755: Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms: 186 (Studies in Computational Intelligence, 186)
Tapa dura
ISBN 10: 3540959750 ISBN 13: 9783540959755
Editorial: Springer, 2009

Sinopsis

Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined.

The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.

"Sinopsis" puede pertenecer a otra edición de este libro.

De la contraportada

Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined.

The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2009
Idioma
Inglés
ISBN 10 
3540959750
ISBN 13 
9783540959755
Encuadernación
Tapa dura
Número de páginas
284
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

Friedrich-Karl-Str. 9
Hamburg
Alemania

Otras ediciones populares con el mismo título

9783642101137: Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms: 186 (Studies in Computational Intelligence)

Edición Destacada

ISBN 10:  3642101135 ISBN 13:  9783642101137
Editorial: Springer, 2010
Tapa blanda