Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization

Dhish Kumar Saxena received the bachelor’s degree in mechanical engineering (1997), the master’s 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 Elsevier’s 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.

• 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.