Overview
- 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 679)
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About this book
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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Keywords
Table of contents (10 chapters)
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Foundations
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Solution Spaces
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Advanced Concepts
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Ending
Authors and Affiliations
Bibliographic Information
Book Title: Genetic Algorithm Essentials
Authors: Oliver Kramer
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-52156-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2017
Hardcover ISBN: 978-3-319-52155-8Published: 13 January 2017
Softcover ISBN: 978-3-319-84834-1Published: 13 July 2018
eBook ISBN: 978-3-319-52156-5Published: 07 January 2017
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: IX, 92
Number of Illustrations: 38 illustrations in colour