LEADER 04414nam 22006255 450 001 9910874692003321 005 20240911041719.0 010 $a9789819732869$b(electronic bk.) 010 $z9789819732852 024 7 $a10.1007/978-981-97-3286-9 035 $a(MiAaPQ)EBC31524279 035 $a(Au-PeEL)EBL31524279 035 $a(CKB)32733256100041 035 $a(DE-He213)978-981-97-3286-9 035 $a(EXLCZ)9932733256100041 100 $a20240711d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntelligent Optimization $ePrinciples, Algorithms and Applications /$fby Changhe Li, Shoufei Han, Sanyou Zeng, Shengxiang Yang 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (369 pages) 311 08$aPrint version: Li, Changhe Intelligent Optimization Singapore : Springer,c2024 9789819732852 327 $achapter 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. 330 $aThis 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. 606 $aArtificial intelligence 606 $aAlgorithms 606 $aMathematical optimization 606 $aComputational intelligence 606 $aArtificial Intelligence 606 $aDesign and Analysis of Algorithms 606 $aContinuous Optimization 606 $aComputational Intelligence 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aMathematical optimization. 615 0$aComputational intelligence. 615 14$aArtificial Intelligence. 615 24$aDesign and Analysis of Algorithms. 615 24$aContinuous Optimization. 615 24$aComputational Intelligence. 676 $a006.3 700 $aLi$b Changhe$01437901 701 $aHan$b Shoufei$01749603 701 $aZeng$b Sanyou$01749604 701 $aYang$b Shengxiang$01736305 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910874692003321 996 $aIntelligent Optimization$94183870 997 $aUNINA