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Metaheuristics for Machine Learning New Advances and Tools

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

14.03.2023

Abbildungen

XV, 223 p. 1 illus.

Herausgeber

Mansour Eddaly + weitere

Verlag

Springer Singapore

Seitenzahl

223

Maße (L/B/H)

24,1/16/1,9 cm

Gewicht

530 g

Auflage

2023

Sprache

Englisch

ISBN

978-981-19-3887-0

Beschreibung

Portrait


Mansour Eddaly
 is an assistant professor in the College of Business and Economics at Qassim University (KSA). His current research interests mainly involve combinatorial optimization, metaheuristics, and computational intelligence.


Bassem Jarboui
 is Full Professor of Operational Research at Sfax University, Tunisia, where he also completed his PhD. Currently, he is working at the Higher Colleges of Technology, Abu Dhabi, UAE. He has edited seven books and two special journal issues. He has also organized and chaired five international conferences. He has published over 130 scientific papers, including articles, contributions to edited proceedings, and book chapters.


Patrick Siarry
 received his PhD from the University of Paris 6 in 1986 and his Doctor of Sciences (
Habilitation
) from the University of Paris 11 in 1994. He first became involved in the development of analogue and digital models of nuclear power plants at Électricité de France (E.D.F.). He has been Professor of Automatics and Informatics since 1995. His main research interest is in the applications of new stochastic global optimization heuristics to various engineering fields.




Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

14.03.2023

Abbildungen

XV, 223 p. 1 illus.

Herausgeber

Verlag

Springer Singapore

Seitenzahl

223

Maße (L/B/H)

24,1/16/1,9 cm

Gewicht

530 g

Auflage

2023

Sprache

Englisch

ISBN

978-981-19-3887-0

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Metaheuristics for Machine Learning
  • Produktbild: Metaheuristics for Machine Learning
  • 1. From metaheuristics to automatic programming.- 2. Biclustering Algorithms Based on Metaheuristics: A Review.- 3. A Metaheuristic Perspective on Learning Classifier Systems.- 4. An evolutionary clustering approach using metaheuristics and unsupervised machine learning algorithms for customer segmentation.- 5. Applications of Metaheuristics in Parameter Optimization in Manufacturing Processes and Machine Health Monitoring.- 6. Evolving Machine Learning-based classifiers by metaheuristic approaches for underwater sonar target detection and recognition.- 7. Solving the Quadratic Knapsack Problem using a GRASP algorithm based on a multi-swap local search.- 8. Algorithmic vs Processing Manipulations to Scale Genetic Programming to Big Data Mining.- 9. Dynamic assignment problem of parking slots.