Handbook of metaheuristic algorithms : from fundamental theories to advanced applications /

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 t...

Full description

Bibliographic Details
Main Author: Tsai, Chun-Wei
Corporate Author: ScienceDirect (Online service)
Other Authors: Chiang, Ming-Chao
Format: eBook
Language:English
Published: London ; San Diego, CA : Academic Press, an imprint of Elsevier, [2023]
Series:Uncertainty, computational techniques, and decision intelligence
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary: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.
Physical Description:1 online resource.
Bibliography:Includes bibliographical references and index.
ISBN:0443191093
9780443191091