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Intelligent optimization = principle...

Li, Changhe.

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Intelligent optimization = principles, algorithms and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Intelligent optimization/ by Changhe Li ... [et al.].
其他題名:	principles, algorithms and applications /
其他作者:	Li, Changhe.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xxiii, 361 p. :ill. (some col.), digital ;24 cm.
內容註:	chapter 1 Introduction -- chapter 2 Fundamentals -- chapter 3 Canonical Optimization Algorithms -- chapter 4 Basics of Evolutionary Computation Algorithms -- chapter 5 Popular Evolutionary Computation Algorithms -- chapter 6 Parameter Control and Policy Control -- chapter 7 Exploitation versus Exploration -- chapter 8 Multi-modal Optimization -- chapter 9 Multi-objective Optimization -- chapter 10 Constrained Optimization -- chapter 11 Dynamic Optimization -- Chapter 12 Robust Optimization -- Chapter 13 Large-scale Global Optimization -- Chapter 14 Expensive Optimization -- Chapter 15 Real-world Applications.
Contained By:	Springer Nature eBook
標題:	Evolutionary computation. -
電子資源:	https://doi.org/10.1007/978-981-97-3286-9
ISBN:	9789819732869

Intelligent optimization = principles, algorithms and applications /
Intelligent optimizationprinciples, algorithms and applications /[electronic resource] :by Changhe Li ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xxiii, 361 p. :ill. (some col.), digital ;24 cm.

chapter 1 Introduction -- chapter 2 Fundamentals -- chapter 3 Canonical Optimization Algorithms -- chapter 4 Basics of Evolutionary Computation Algorithms -- chapter 5 Popular Evolutionary Computation Algorithms -- chapter 6 Parameter Control and Policy Control -- chapter 7 Exploitation versus Exploration -- chapter 8 Multi-modal Optimization -- chapter 9 Multi-objective Optimization -- chapter 10 Constrained Optimization -- chapter 11 Dynamic Optimization -- Chapter 12 Robust Optimization -- Chapter 13 Large-scale Global Optimization -- Chapter 14 Expensive Optimization -- Chapter 15 Real-world Applications.

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 optimization problems. Recognizing the importance of hands-on experience, the textbook introduces the Open-source Framework for Evolutionary Computation platform (OFEC) as a user-friendly tool. This platform serves as a comprehensive toolkit for implementing, evaluating, visualizing, and benchmarking various optimization algorithms. The book guides readers on maximizing the utility of OFEC for conducting experiments and analyses in the field of evolutionary computation, facilitating a deeper understanding of intelligent optimization through practical application.

ISBN: 9789819732869

Standard No.: 10.1007/978-981-97-3286-9doiSubjects--Topical Terms:

582189
Evolutionary computation.

LC Class. No.: QA76.618

Dewey Class. No.: 006.3

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505 0 $a chapter 1 Introduction -- chapter 2 Fundamentals -- chapter 3 Canonical Optimization Algorithms -- chapter 4 Basics of Evolutionary Computation Algorithms -- chapter 5 Popular Evolutionary Computation Algorithms -- chapter 6 Parameter Control and Policy Control -- chapter 7 Exploitation versus Exploration -- chapter 8 Multi-modal Optimization -- chapter 9 Multi-objective Optimization -- chapter 10 Constrained Optimization -- chapter 11 Dynamic Optimization -- Chapter 12 Robust Optimization -- Chapter 13 Large-scale Global Optimization -- Chapter 14 Expensive Optimization -- Chapter 15 Real-world Applications.
520 $a 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 optimization problems. Recognizing the importance of hands-on experience, the textbook introduces the Open-source Framework for Evolutionary Computation platform (OFEC) as a user-friendly tool. This platform serves as a comprehensive toolkit for implementing, evaluating, visualizing, and benchmarking various optimization algorithms. The book guides readers on maximizing the utility of OFEC for conducting experiments and analyses in the field of evolutionary computation, facilitating a deeper understanding of intelligent optimization through practical application.
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