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Añadir al carritoPaperback. Condición: Brand New. 500 pages. 9.00x6.00 inches. In Stock.
Publicado por Elsevier Science Publishing Co Inc, 2023
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Añadir al carritoCondición: New. 2023. 1st Edition. Paperback. . . . . .
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Añadir al carritoCondición: New. InhaltsverzeichnisPART 1 Fundamentals 1. Introduction 2. Optimization problems 3. Traditional methods 4. Metaheuristic algorithms 5. Simulated annealing 6. Tabu search 7. Genetic algori.
Publicado por Elsevier Science Publishing Co Inc Jun 2023, 2023
ISBN 10: 0443191085 ISBN 13: 9780443191084
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - 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.
Publicado por Elsevier Science & Technology, 2023
ISBN 10: 0443191085 ISBN 13: 9780443191084
Idioma: Inglés
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Añadir al carritoCondición: New. 2023. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.
Publicado por Elsevier Science Publishing Co Inc, US, 2023
ISBN 10: 0443191085 ISBN 13: 9780443191084
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Añadir al carritoPaperback. Condición: 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.
Publicado por Elsevier Science Publishing Co Inc, US, 2023
ISBN 10: 0443191085 ISBN 13: 9780443191084
Idioma: Inglés
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
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Añadir al carritoPaperback. Condición: 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.
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Añadir al carritoCondición: new. Questo è un articolo print on demand.