Natural Computing Series

Multimodal Optimization by Means of Evolutionary Algorithms

Autoren: Preuss, Mike

  • Describes state of the art in algorithms, measures and test problems
  • Approaches multimodal optimization algorithms via model-based simulation and statistics
  • Valuable for practitioners with real-world black-box problems
Weitere Vorteile

Dieses Buch kaufen

eBook 83,29 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-07407-8
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: PDF
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 106,99 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-07406-1
  • Kostenfreier Versand für Individualkunden weltweit
  • Gewöhnlich versandfertig in 3-5 Werktagen.
Über dieses Buch

This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization.

The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used.

The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.

Über den Autor

Dr. Mike Preuss got his Ph.D. in the Technische Universität Dortmund and he is now a researcher at the Westfälische Wilhelms-Universität Münster. He has published in the leading journals and conferences on various aspects of computational intelligence, in particular evolutionary computing, heuristics, search and multicriteria optimization and served on many of the key academic conference committees, journal boards and review committees in this field. He is a leading figure in the application of computational and artificial intelligence to games.

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“It provides an excellent explanation of the theoretical background of many topics in evolutionary computation … . I strongly recommend this book for graduate students or any researcher who wants to work in the EC field … . It also may help in improving some algorithms and may motivate the researcher to introduce new ones. … the chapters are self-contained so that you can read individual chapters that you are interested in without the need to read the whole book.” (Nailah Al-Madi, Genetic Programming and Evolvable Machines, Vol. 17 (3), September, 2016)


Inhaltsverzeichnis (7 Kapitel)

Dieses Buch kaufen

eBook 83,29 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-07407-8
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: PDF
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 106,99 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-07406-1
  • Kostenfreier Versand für Individualkunden weltweit
  • Gewöhnlich versandfertig in 3-5 Werktagen.
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Bibliografische Information

Bibliographic Information
Buchtitel
Multimodal Optimization by Means of Evolutionary Algorithms
Autoren
Titel der Buchreihe
Natural Computing Series
Copyright
2015
Verlag
Springer International Publishing
Copyright Inhaber
Springer International Publishing Switzerland
eBook ISBN
978-3-319-07407-8
DOI
10.1007/978-3-319-07407-8
Hardcover ISBN
978-3-319-07406-1
Buchreihen ISSN
1619-7127
Auflage
1
Seitenzahl
XX, 189
Anzahl der Bilder
37 schwarz-weiß Abbildungen, 5 Abbildungen in Farbe
Themen