國立高雄大學圖資館 |

Language: English

Back

Intelligent optimizationprinciples, algorithms and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent optimizationby 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.
Contained By:	Springer Nature eBook
Subject:	Evolutionary computation.
Online resource:	https://doi.org/10.1007/978-981-97-3286-9
ISBN:	9789819732869$q(electronic bk.)

Intelligent optimizationprinciples, 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$q(electronic bk.)

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

231709
Evolutionary computation.

LC Class. No.: QA76.618

Dewey Class. No.: 006.3

Intelligent optimizationprinciples, algorithms and applications /
LDR:03378nmm a2200325 a 4500 001 666635
003 DE-He213
005 20240711125259.0
006 m d
007 cr nn 008maaau
008 250107s2024 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 231709
650 0 $a Mathematical optimization. $3 183292
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Design and Analysis of Algorithms. $3 913142
650 2 4 $a Continuous Optimization. $3 558846
650 2 4 $a Computational Intelligence. $3 338479
700 1 $a Li, Changhe. $3 972156
710 2 $a SpringerLink (Online service) $3 273601
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)