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Handbook of Metaheuristic Algorithms : From Fundamental Theories to Advanced Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications (Uncertainty, Computational Techniques, and Decision Intelligence)
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Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications (Uncertainty, Computational Techniques, and Decision Intelligence)
Published by Academic Press 2023-06-15, 2023
ISBN 10: 0443191085 ISBN 13: 9780443191084
Language: English
Paperback. Condition: New.
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Handbook of Metaheuristic Algorithms : From Fundamental Theories to Advanced Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Paperback. Condition: Brand New. 500 pages. 9.00x6.00 inches. In Stock.
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Add to basketCondition: New. InhaltsverzeichnisPART 1 Fundamentals 1. Introduction 2. Optimization problems 3. Traditional methods 4. Metaheuristic algorithms 5. Simulated annealing 6. Tabu search 7. Genetic algori.
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Handbook of Metaheuristic Algorithms
Published by Elsevier Science Publishing Co Inc, US, 2023
ISBN 10: 0443191085 ISBN 13: 9780443191084
Language: English
Paperback. Condition: 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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Handbook of Metaheuristic Algorithms
Published by Elsevier Science Publishing Co Inc, US, 2023
ISBN 10: 0443191085 ISBN 13: 9780443191084
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: 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.
