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Record Nr. |
UNINA9911003689003321 |
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Autore |
Das Debashish |
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Titolo |
Optimization algorithms in machine learning : a meta-heuristics perspective / / by Debashish Das, Ali Safaa Sadiq, Seyedali Mirjalili |
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Pubbl/distr/stampa |
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Singapore : , : Springer, , [2025] |
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©2025 |
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ISBN |
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Descrizione fisica |
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1 online resource (xvii, 181 pages) : illustrations |
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Collana |
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Engineering optimization: methods and applications, , 2731-4057 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Machine learning |
Mathematical optimization |
Computational Intelligence |
Machine Learning |
Optimization |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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. . |
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