- 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
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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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Bibliografische Information
- Bibliographic Information
-
- Buchtitel
- Genetic Algorithm Essentials
- Autoren
-
- Oliver Kramer
- Titel der Buchreihe
- Studies in Computational Intelligence
- Buchreihen Band
- 679
- Copyright
- 2017
- Verlag
- Springer International Publishing
- Copyright Inhaber
- Springer International Publishing AG, part of Springer Nature
- eBook ISBN
- 978-3-319-52156-5
- DOI
- 10.1007/978-3-319-52156-5
- Hardcover ISBN
- 978-3-319-52155-8
- Softcover ISBN
- 978-3-319-84834-1
- Buchreihen ISSN
- 1860-949X
- Auflage
- 1
- Seitenzahl
- IX, 92
- Anzahl der Bilder
- 38 Abbildungen in Farbe
- Themen
