Genetic algorithm essentials

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 over...

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Bibliographic Details
Main Author Kramer, Oliver (Author)
Format Electronic eBook
LanguageEnglish
Published Cham, Switzerland : Springer, 2017.
SeriesStudies in computational intelligence ; v. 679.
Subjects
Online AccessFull text
ISBN9783319521565
9783319521558
ISSN1860-949X ;
Physical Description1 online resource (ix, 92 pages) : color illustrations

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100 1 |a Kramer, Oliver,  |e author. 
245 1 0 |a Genetic algorithm essentials /  |c Oliver Kramer. 
264 1 |a Cham, Switzerland :  |b Springer,  |c 2017. 
300 |a 1 online resource (ix, 92 pages) :  |b color illustrations 
336 |a text  |b txt  |2 rdacontent 
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338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Studies in computational intelligence,  |x 1860-949X ;  |v volume 679 
504 |a Includes bibliographical references and index. 
505 0 |a 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. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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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830 0 |a Studies in computational intelligence ;  |v v. 679.  |x 1860-949X 
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