Into a Deeper Understanding of Evolutionary Computing: Exploration, Exploitation, and Parameter Control: Volume 2

Abdul Halim, Abdul Hanif (Author)/ Das, Swagatam (Author)/ Ismail, Idris (Author)

ISBN 10: 3031755766 / ISBN 13: 9783031755767

Published by Springer, 2025

About this Item

336 pages. 9.25x6.10x9.49 inches. In Stock.

Seller Inventory # __3031755766

Bibliographic Details

• Title: Into a Deeper Understanding of Evolutionary Computing: Exploration, Exploitation, and Parameter Control: Volume 2
• Publisher: Springer
• Publication Date: 2025
• Language: English
• ISBN 10: 3031755766
• ISBN 13: 9783031755767
• Binding: Hardcover
• Condition: Brand New
• Weight: 0.79 kilograms

About this title

Synopsis

This book delves into fundamental and advanced strategies for enhancing evolutionary and metaheuristic algorithms, focusing on the crucial balance between exploration and exploitation in search mechanisms. As technological advancements increase optimization complexity, effectively managing this balance becomes essential for achieving optimal solutions within reasonable computational resources. The book's distinctive structure organizes content according to optimization stages and processes, offering a comprehensive discussion of various approaches supported by extensive literature.

The authors note a scarcity of literature addressing the trade-offs between exploration and exploitation with contemporary references, making this work particularly valuable. It aims to deepen the reader's understanding of evolutionary computing, emphasizing exploration, exploitation, and parameter control. It is relevant not only to algorithm developers and the evolutionary computation community but also to students and researchers across scientific disciplines. The book is designed to be accessible to those without extensive algorithm development backgrounds, providing theoretical and practical insights into optimization methods.

From the Back Cover

This book delves into fundamental and advanced strategies for enhancing evolutionary and metaheuristic algorithms, focusing on the vital balance between exploration and exploitation search mechanisms. As technological advancements increase optimization complexity, effectively managing this balance becomes crucial for achieving optimal solutions within reasonable computational resources. The book's distinctive structure organizes content according to optimization stages, offering a comprehensive discussion of various approaches supported by extensive literature.