東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Intelligent optimization = principle...

Li, Changhe.

Linked to FindBook

Google Book

Amazon

博客來

Intelligent optimization = principles, algorithms and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent optimization/ by Changhe Li ... [et al.].
Reminder of title:	principles, algorithms and applications /
other author:	Li, Changhe.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xxiii, 361 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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
Subject:	Evolutionary computation. -
Online resource:	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

Intelligent optimization = principles, algorithms and applications /
LDR:03377nmm a2200325 a 4500 001 2373858
003 DE-He213
005 20240711125259.0
006 m d
007 cr nn 008maaau
008 241231s2024 si s 0 eng d
020 $a 9789819732869 $q (electronic bk.)
020 $a 9789819732852 $q (paper)
024 7 $a 10.1007/978-981-97-3286-9 $2 doi
035 $a 978-981-97-3286-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.618
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a QA76.618 $b .I61 2024
245 0 0 $a Intelligent optimization $h [electronic resource] : $b principles, algorithms and applications / $c by Changhe Li ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xxiii, 361 p. : $b ill. (some col.), digital ; $c 24 cm.
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.
650 0 $a Evolutionary computation. $3 582189
650 0 $a Mathematical optimization. $3 517763
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Design and Analysis of Algorithms. $3 3538532
650 2 4 $a Continuous Optimization. $3 1566748
650 2 4 $a Computational Intelligence. $3 1001631
700 1 $a Li, Changhe. $3 3711474
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-97-3286-9
950 $a Computer Science (SpringerNature-11645)