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Genetic Algorithm Essentials de Oliver Kramer

Genetic Algorithm Essentials: Studies in Computational Intelligence, cartea 679

Autor Oliver Kramer
en Limba Engleză Hardback – 13 ian 2017
This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations.

The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
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Specificații

ISBN-13: 9783319521558
ISBN-10: 3319521551
Pagini: 92
Ilustrații: IX, 92 p. 38 illus. in color.
Dimensiuni: 155 x 235 x 8 mm
Greutate: 0.33 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Part I: Foundations.- Introduction.- Genetic Algorithms.- Parameters.- Part II: Solution Spaces.- Multimodality.- Constraints.- Multiple Objectives.- Part III: Advanced Concepts.- Theory.- Machine Learning.- Applications.- Part IV: Ending.- Summary and Outlook.- Index.- References.

Textul de pe ultima copertă

This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations.

The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

Caracteristici

Provides an essential introduction to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible Presents an overview of strategies for tuning and controlling parameters Includes a brief introduction to theoretical tools for GAs, the intersections and hybridizations with machine learning, and a selection of promising applications Includes supplementary material: sn.pub/extras