04524nam a22005415i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001300172050001400185072001600199072002300215072001500238100008900253245017900342250001800521264007200539300006400611336002600675337002600701338003600727347002400763490005300787505044400840520191701284650003603201650002903237650003903266650003603305650002903341650003903370700008403409700008403493700008503577710003403662773002603696776003603722776003603758776003603794830006003830856004603890912001403936912001503950999001703965978-981-99-2096-9DE-He21320251030112047.0cr nn 008mamaa240517s2024 si | s |||| 0|eng d a97898199209699978-981-99-2096-97 a10.1007/978-981-99-2096-92doi 4aQ334-342 4aTA347.A78 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aSaxena, Dhish Kumar.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97697510aMachine Learning Assisted Evolutionary Multi- and Many- Objective Optimizationh[electronic resource] /cby Dhish Kumar Saxena, Sukrit Mittal, Kalyanmoy Deb, Erik D. Goodman. a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024. aXV, 244 p. 83 illus., 53 illus. in color.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda1 aGenetic and Evolutionary Computation,x1932-01750 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. 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. 0aArtificial intelligence.958441 0aMachine learning.955059 0aComputational intelligence.96405214aArtificial Intelligence.95844124aMachine Learning.95505924aComputational Intelligence.9640521 aMittal, Sukrit.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9769761 aDeb, Kalyanmoy.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9769771 aGoodman, Erik D.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9769782 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981992095208iPrinted edition:z978981992097608iPrinted edition:z9789819920983 0aGenetic and Evolutionary Computation,x1932-017596695040uhttps://doi.org/10.1007/978-981-99-2096-9 aZDB-2-SCS aZDB-2-SXCS c59934d59934