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Optimization Algorithms in Machine Learning : A Meta-heuristics Perspective / by Debashish Das, Ali Safaa Sadiq, Seyedali Mirjalili

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Engineering Optimization: Methods and ApplicationsPublisher: Singapore : Springer Nature Singapore, 2025Publisher: Singapore : Imprint: Springer, 2025Edition: 1st ed. 2025Description: 1 Online-Ressource(XVII, 181 p. 45 illus., 10 illus. in color.)ISBN:
  • 9789819638499
Subject(s): Additional physical formats: 9789819638482 | 9789819638505 | 9789819638512 | Erscheint auch als: 9789819638482 Druck-Ausgabe | Erscheint auch als: 9789819638505 Druck-Ausgabe | Erscheint auch als: 9789819638512 Druck-AusgabeDDC classification:
  • 006.3 23
DOI: DOI: 10.1007/978-981-96-3849-9Online resources: Summary: Challenges and opportunities in Machine Learning using optimization techniques -- Optimization methods: traditional versus stochastic -- Heuristic and meta-heuristic optimization algorithms -- A comprehensive review of evolutionary algorithms and swarm intelligence methods -- Artificial Neural Networks: structure and learning -- A survey of Neural Networks trained by optimization algorithms and meta-heuristics.Summary: This book explores the development of several new learning algorithms that utilize recent optimization techniques and meta-heuristics. It addresses well-known models such as particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, population-based incremental learning, and grey wolf optimizer for training neural networks. Additionally, the book examines the challenges associated with these processes in detail. This volume will serve as a valuable reference for individuals in both academia and industry. .PPN: PPN: 1926698207Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT
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