Optimization algorithms in machine learning : a meta-heuristics perspective / / by Debashish Das, Ali Safaa Sadiq, Seyedali Mirjalili

• Autore: Das Debashish
• Pubblicazione: Singapore : , : Springer, , [2025]; ©2025
• Descrizione fisica: 1 online resource (xvii, 181 pages) : illustrations
• Disciplina: 006.3
• Soggetto topico: Computational intelligence; Machine learning; Mathematical optimization; Computational Intelligence; Machine Learning; Optimization
• Persona (resp. second.): SadiqAli Safaa; MirjaliliSeyedali
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 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.
• Sommario/riassunto: 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. .
• Titolo autorizzato: Optimization Algorithms in Machine Learning
• ISBN: 9789819638499
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911003689003321
• Lo trovi qui: Univ. Federico II
• Serie: Engineering optimization: methods and applications. . 2731-4057