Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization b

Description: Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization by Dhish Kumar Saxena, Kalyanmoy Deb, Erik D. Goodman, Sukrit Mittal This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMâO). 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. FORMAT Hardcover CONDITION Brand New Publisher Description This 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. Author Biography Dhish Kumar Saxena received the bachelors degree in mechanical engineering (1997), the masters degree in solid mechanics and design (1999), and the Ph.D. degree in evolutionary many-objective optimization (2008) from the Indian Institute of Technology Kanpur, India. Currently, he is a Professor at the Department of Mechanical and Industrial Engineering, and a joint faculty at the Mehta Family of Data Science and Artificial Intelligence, Indian Institute of Technology (IIT) Roorkee, India. Prior to joining IIT Roorkee, he worked with the Cranfield University and Bath University, U.K., from 2008 to 2012. At a fundamental level, his research has focused on Multi- and Many-objective optimization, including, development of Evolutionary Algorithms and their performance enhancement using Machine Learning; Termination criterion for these algorithms; and Decision Support based on objectives and constraints relative preferences. At an applied level, his focus has been on demonstrating the utility of Evolutionary and Mathematical Optimization on a range of real-world problems, including scheduling, engineering design, business-process, and multi-criterion decision making. He is also an Associate Editor for Elseviers Swarm and Evolutionary Computation journal.Sukrit Mittal is a Senior Research Scientist in the AI & Optimization Research team at Franklin Templeton Investments. He obtained his B.Tech. (2012-16) and Ph.D. (2018-22) degrees from IIT Roorkee, India. He also worked with Mahindra Research Valley as a design engineer (2016-18). His research has primarily focused on evolutionary multi- and many-objective optimization, machine learning assisted optimization, and innovization.Kalyanmoy Deb is University Distinguished Professor and Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. His research interests are in evolutionary optimization and their application inmulti-criterion optimization, modeling, and machine learning. He was awarded IEEE Evolutionary Computation Pioneer Award for his sustained work in EMO, Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He is fellow of IEEE and ASME. Erik D. Goodman was PI and Director of BEACON Center for the Study of Evolution in Action, an NSF Center headquartered at Michigan State University, 2010-2018. He was Professor of Electrical & Computer Engineering, also Mechanical Engineering and Computer Science & Engineering, until retiring in 2022. He co-founded Red Cedar Technology (1999, now part of Siemens), and developed the HEEDS SHERPA commercial design optimization software. Honors include Michigan Distinguished Professor of the Year, 2009; MSU Distinguished Faculty Award, 2011; Senior Fellow, International Society for Genetic and Evolutionary Computation, 2004; Founding Chair, ACM SIG on Genetic and Evolutionary Computation (SIGEVO), 2005-2007. Table of Contents Introduction.- 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. Details ISBN9819920957 Author Sukrit Mittal Publisher Springer Verlag, Singapore Series Genetic and Evolutionary Computation ISBN-13 9789819920952 Format Hardcover Imprint Springer Verlag, Singapore Place of Publication Singapore Country of Publication Singapore Year 2024 Alternative 9789819920983 Audience Professional & Vocational Pages 244 DEWEY 006.31 Publication Date 2024-05-18 Illustrations 53 Illustrations, color; 30 Illustrations, black and white; XV, 244 p. 83 illus., 53 illus. in color. Edition Description 2024 ed. Edition 2024th We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:160116250;

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