Advanced Techniques for Modifying Metaheuristics: Methods and Applications
暫譯: 修改元啟發式演算法的進階技術:方法與應用
Razmjooy, Navid
- 出版商: Morgan Kaufmann
- 出版日期: 2026-01-01
- 售價: $6,190
- 貴賓價: 9.5 折 $5,880
- 語言: 英文
- 頁數: 350
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0443329729
- ISBN-13: 9780443329722
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
商品描述
Metaheuristics are widely used optimization techniques that have been successfully applied in various real-world problems. However, no single metaheuristic algorithm can solve all optimization problems with the same level of efficiency and effectiveness. Advanced Techniques for Modifying Metaheuristics: Methods and Applications covers the latest developments in the field of metaheuristics modification, including theoretical aspects, empirical studies, and practical applications. The book is organized into four main parts, introducing metaheuristics and their basic concepts, the theory and principles of modifying metaheuristics, empirical studies and experimental evaluations of modified metaheuristics, and practical applications of modified metaheuristics in various fields. The modification of metaheuristics has been shown to be a promising approach for improving their performance in solving complex optimization problems. However, there is still a need for more advanced and effective techniques for modifying metaheuristics. This book provides a critical analysis of the strengths and weaknesses of different modification techniques, as well as their suitability for different types of optimization problems. It also covers the latest developments in the field, including the use of machine learning and artificial intelligence techniques for modifying metaheuristics.
商品描述(中文翻譯)
元啟發式演算法是廣泛使用的優化技術,已成功應用於各種現實世界的問題。然而,沒有任何單一的元啟發式演算法能以相同的效率和效果解決所有的優化問題。《進階元啟發式演算法修改技術:方法與應用》涵蓋了元啟發式演算法修改領域的最新發展,包括理論方面、實證研究和實際應用。本書分為四個主要部分,介紹元啟發式演算法及其基本概念、修改元啟發式演算法的理論與原則、修改元啟發式演算法的實證研究與實驗評估,以及修改元啟發式演算法在各個領域的實際應用。元啟發式演算法的修改已被證明是一種有前景的方法,可以改善其在解決複雜優化問題中的表現。然而,仍然需要更先進和有效的技術來修改元啟發式演算法。本書對不同修改技術的優缺點進行了批判性分析,以及它們對不同類型優化問題的適用性。它還涵蓋了該領域的最新發展,包括使用機器學習和人工智慧技術來修改元啟發式演算法。