LEADER 01049nam0 2200265 i 450 001 SUN0044759 005 20180419104144.572 010 $a04-7190-472-4 100 $a20060502d1984 |0engc50 ba 101 $aeng 102 $aGB 105 $a|||| ||||| 200 1 $aModal testing$etheory and practice$fD. J. Ewins 210 $aTaunton$cResearch Studies$aNew York$cWiley$d1984 215 $aXIV, 313 p.$cill.$d24 cm. 410 1$1001SUN0044757$12001 $aMechanical engineering research studies$1210 $aTaunton$cResearch Studies. 410 1$1001SUN0044758$12001 $aEngineering dynamics series$v2$1210 $aTaunton$cResearch Studies. 620 $aUS$dNew York$3SUNL000011 700 1$aEwins$b, D. J.$3SUNV036079$0442660 712 $aWiley$3SUNV000201$4650 801 $aIT$bSOL$c20201005$gRICA 912 $aSUN0044759 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI INGEGNERIA$d05CONS J I 078 $e05 2416 20060502 996 $aModal testing$91396459 997 $aUNICAMPANIA LEADER 04345nam 22006135 450 001 9910861099503321 005 20240517125435.0 010 $a981-9920-96-5 024 7 $a10.1007/978-981-99-2096-9 035 $a(MiAaPQ)EBC31345503 035 $a(Au-PeEL)EBL31345503 035 $a(CKB)32074570800041 035 $a(DE-He213)978-981-99-2096-9 035 $a(OCoLC)1434648246 035 $a(EXLCZ)9932074570800041 100 $a20240517d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning Assisted Evolutionary Multi- and Many- Objective Optimization /$fby Dhish Kumar Saxena, Sukrit Mittal, Kalyanmoy Deb, Erik D. Goodman 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (253 pages) 225 1 $aGenetic and Evolutionary Computation,$x1932-0175 311 08$a981-9920-95-7 327 $aIntroduction -- Optimization Problems and Algorithms -- Existing Machine Learning Studies on Multi-objective Optimization -- Learning to Converge Better and Faster -- Learning to Diversify Better and Faster -- Learning to Simultaneously Converge and Diversify Better and Faster -- Learning to Understand the Problem Structure -- ML-Assisted Analysis of Pareto-optimal Front -- Further Machine Learning Assisted Enhancements -- Conclusions. 330 $aThis book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMâO). EMâO algorithms, namely EMâOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMâOAs amenable to application of ML for different pursuits. Recognizing the immense potential for ML-based enhancements in the EMâO domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners. To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types. Then, well-structured chapters present some of the key studies on ML-based enhancements in the EMâO domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23). It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EMâOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMâOA domain. To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EMâOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMâOA and ML domains. 410 0$aGenetic and Evolutionary Computation,$x1932-0175 606 $aArtificial intelligence 606 $aMachine learning 606 $aComputational intelligence 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aComputational Intelligence 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aComputational intelligence. 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aComputational Intelligence. 676 $a006.3 700 $aSaxena$b Dhish Kumar$01739483 701 $aMittal$b Sukrit$01739484 701 $aDeb$b Kalyanmoy$0725709 701 $aGoodman$b Erik D$01739485 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910861099503321 996 $aMachine Learning Assisted Evolutionary Multi- and Many- Objective Optimization$94163486 997 $aUNINA