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This Book Introduces The Theory And Applications Of Metaheuristic Algorithms. It Also Provides Methods For Solving Practical Problems Such As Software Engineering Problems, Image Recognition Problems, Problems In Video Networks, And Problems In The Ocean. In The Theoretical Section, The Book Introduces The Information Feedback Model, Learning-based Intelligent Optimization, Dynamic Multi-objective Optimization, And Multi-model Optimization. In The Applications Section, The Book Presents Applications Of Optimization Algorithms To Neural Architecture Search, Fuzz Testing, Oceans, And Image Processing. The Neural Architecture Search Section Introduces The Latest Nas Method. The Fuzz Testing Section Uses Multiobjective Optimization And Ant Colony Optimization To Solve The Seed Selection And Energy Allocation Problems In Fuzz Testing. In The Ocean Section, Deep Learning Methods Such As Cnn, Transformer, And Attention-based Methods Are Used To Describe Enso Prediction And Image Processing For Marine Fish Identification, And To Provide An Overview Of Traditional Classification Methods And Deep Learning Methods. Rich In Examples, This Book Will Be A Great Resource For Students, Scholars, And Those Interested In Metaheuristic Algorithms, As Well As Professional Practitioners And Researchers Working On Related Topics-- Provided By Publisher.
This book investigates the theoretical underpinnings and practical applications of metaheuristic algorithms. It presents advanced optimization techniques, including the Information Feedback Model, learning-based intelligent optimization, dynamic multi-objective optimization, and multi-model optimization. The text then details applications in diverse fields such as software engineering, image recognition, video networks, and oceanography, offering solutions to real-world challenges.
The book is structured to serve as a comprehensive resource for students, scholars, and practitioners interested in metaheuristic algorithms. It balances theoretical depth with practical examples, covering recent advancements like Neural Architecture Search (NAS) and the application of multiobjective and ant colony optimization in fuzz testing. The inclusion of deep learning methods for Enso prediction and marine fish identification in the oceanography section highlights its contemporary relevance.
Page Count:
0
Publication Date:
2024-01-01
Publisher:
Boca Raton, FL : CRC Press,
ISBN-10:
100342242X
ISBN-13:
9781003422426
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