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Titlebook: Intelligent Optimization; Principles, Algorith Changhe Li,Shoufei Han,Shengxiang Yang Textbook 2024 China University of Geosciences Press 2

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發(fā)表于 2025-3-21 18:47:37 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Intelligent Optimization
副標題Principles, Algorith
編輯Changhe Li,Shoufei Han,Shengxiang Yang
視頻videohttp://file.papertrans.cn/470/469862/469862.mp4
概述Fundamental and typical intelligent optimization algorithms for a wide range of learners.Practical significance of cutting-edge applications for a diverse range of optimization problems in real-world
圖書封面Titlebook: Intelligent Optimization; Principles, Algorith Changhe Li,Shoufei Han,Shengxiang Yang Textbook 2024 China University of Geosciences Press 2
描述.This textbook comprehensively explores the foundational principles, algorithms, and applications of intelligent optimization, making it an ideal resource for both undergraduate and postgraduate artificial intelligence courses. It remains equally valuable for active researchers and individuals engaged in self-study. Serving as a significant reference, it delves into advanced topics within the evolutionary computation field, including multi-objective optimization, dynamic optimization, constrained optimization, robust optimization, expensive optimization, and other pivotal scientific studies related to optimization...?..Designed to be approachable and inclusive, this textbook equips readers with the essential mathematical background necessary for understanding intelligent optimization. It employs an accessible writing style, complemented by extensive pseudo-code and diagrams that vividly illustrate the mechanisms, principles, and algorithms of optimization. With a focus on practicality, this textbook provides diverse real-world application examples spanning engineering, games, logistics, and other domains, enabling readers to confidently apply intelligent techniques to actual optimi
出版日期Textbook 2024
關鍵詞Artificial Intelligence; Optimization; Computational Intelligence; Evolutionary Computation; Swarm Intel
版次1
doihttps://doi.org/10.1007/978-981-97-3286-9
isbn_softcover978-981-97-3285-2
isbn_ebook978-981-97-3286-9
copyrightChina University of Geosciences Press 2024
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沙發(fā)
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板凳
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Basics of Evolutionary Computation Algorithms computation (EC) is reviewed, including some biological processes that have inspired researchers. Classic evolutionary procedures, including representation, selection, and reproduction, are described in detail.
地板
發(fā)表于 2025-3-22 07:15:57 | 只看該作者
Parameter Control and Policy Control Single-parameter-specific methods and multi-parameter ensemble methods are introduced. The commonly used policy control frame is explained, and methods for operator selection control and hyper-heuristics control are discussed. Finally, promising trends in parameter and policy control are provided.
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Exploitation Versus ExplorationThis chapter will first introduce the concepts of exploration and exploitation by reviewing several typical optimization algorithms. Next, methods of enhancing exploration or exploitation in EAs will be discussed. The final part will introduce existing methods of how to balance evolutionary exploration and exploitation.
7#
發(fā)表于 2025-3-22 17:04:24 | 只看該作者
Multimodal OptimizationThis chapter will first introduce the definition of multimodal optimization. Next, most representative evolutionary multimodal optimization algorithms, which are also known as niching methods, will be introduced. Future challenges will be discussed in the final part.
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發(fā)表于 2025-3-22 21:18:50 | 只看該作者
Constrained OptimizationIn this chapter, we will introduce the concept of constrained optimization problems(COPs), commonly used constraint-handling techniques based on EAs, and future research on constrained optimization.
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發(fā)表于 2025-3-23 05:17:48 | 只看該作者
Expensive OptimizationIn this chapter, we will first introduce the concept of expensive optimization. Some surrogate models and model management strategies in surrogate-assisted EAs are then described in detail. Finally, some challenges in the field of expensive optimization are discussed.
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