Kramer Genetic Algorithm Essentials

1. Auflage 2017
ISBN: 978-3-319-52156-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 679, 92 Seiten, eBook

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Kramer, Oliver

Inhaltsverzeichnis

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.