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Da: Majestic Books, Hounslow, Regno Unito
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 500 pages. 9.00x6.00 inches. In Stock.
Da: Books Puddle, New York, NY, U.S.A.
EUR 167,08
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Da: Chiron Media, Wallingford, Regno Unito
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: moluna, Greven, Germania
EUR 170,26
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Aggiungi al carrelloCondizione: New. InhaltsverzeichnisPART 1 Fundamentals 1. Introduction 2. Optimization problems 3. Traditional methods 4. Metaheuristic algorithms 5. Simulated annealing 6. Tabu search 7. Genetic algori.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 177,37
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Editore: Elsevier Science Publishing Co Inc, US, 2023
ISBN 10: 0443191085 ISBN 13: 9780443191084
Lingua: Inglese
Da: Rarewaves.com UK, London, Regno Unito
EUR 224,67
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Aggiungi al carrelloPaperback. Condizione: New. Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains. Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.
Editore: Elsevier Science Publishing Co Inc, US, 2023
ISBN 10: 0443191085 ISBN 13: 9780443191084
Lingua: Inglese
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 242,05
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Aggiungi al carrelloPaperback. Condizione: New. Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains. Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.