Над 4 милиона заглавия на английски и други езици! Открийте новата си история още днес! Безплатна доставка за поръчки над 69.99€

Виж статуса на поръчката

Свържете се с нас

Representations for Genetic and Evolutionary Algorithms

Name: Representations for Genetic and Evolutionary Algorithms
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783540250593
Price: 157.99 EUR
Availability: InStock

Автор: Franz Rothlauf

Език:

Корици: С твърди корици

Издател: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Наличност: Външен склад

Изпращаме след 10-13 дни

157.99 € 309.01 лв

In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical s...

Информация за книгата

Автор

Franz Rothlauf

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2005

страници

325

EAN

9783540250593

ISBN

354025059X

Enbook ID

01560374

Издател

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Теглоt

682

Размери

155 x 235 x 23

Пълно описание

In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has been focused on operators and test problems, while problem representation has often been taken as given. This book breaks with this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance. The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently. The book is written in an easy-readable style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has focused on operators and test problems, while problem representation has often been taken as given. This book breaks away from this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance.The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently.The book is written in an easy-to-read style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.