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Genetic algorithm essentials

Kramer, Oliver.

Genetic algorithm essentials

Record Type:	Electronic resources : Monograph/item
Title/Author:	Genetic algorithm essentialsby Oliver Kramer.
Author:	Kramer, Oliver.
Published:	Cham :Springer International Publishing :2017.
Description:	ix, 92 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Genetic algorithms.
Online resource:	http://dx.doi.org/10.1007/978-3-319-52156-5
ISBN:	9783319521565$q(electronic bk.)

Genetic algorithm essentials
Kramer, Oliver.

Genetic algorithm essentials[electronic resource] /by Oliver Kramer. - Cham :Springer International Publishing :2017. - ix, 92 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.6791860-949X ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9783319521565$q(electronic bk.)

Standard No.: 10.1007/978-3-319-52156-5doiSubjects--Topical Terms:

182939
Genetic algorithms.

LC Class. No.: QA402.5

Dewey Class. No.: 519.625

Genetic algorithm essentials
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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.
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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