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Genetic Algorithm Essentials

  • Book
  • © 2017

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

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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Keywords

Table of contents (10 chapters)

  1. Foundations

  2. Solution Spaces

  3. Advanced Concepts

  4. Ending

Authors and Affiliations

  • Department für Informatik, Abteilung Computational Intelligence, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany

    Oliver Kramer

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

  • Topics: Computational Intelligence, Artificial Intelligence

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