Kalyanmoy Deb is University Distinguished Professor and Koenig Endowed Chair Professor of Department of Electrical and Computer Engineering at Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning. He has been working in evolutionary computation and optimization fields for the past 35 years. He has been a visiting professor at various universities across the world including University of Skövde in Sweden, Aalto University in Finland, Nanyang Technological University in Singapore, and IITs in India. He was awarded IEEE Evolutionary Computation Pioneer Award for his sustained work in evolutionary multi-criterion optimization (EMO), Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Distinguished Alumni Award from IIT Kharagpur, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He received honorary Doctorate degree from University of Jyvaskyla, Finland in 2013. He is fellow of ACM, IEEE, ASME, and three Indian science and engineering academies. He has published over 620 research papers with Google Scholar citation of over 190,000 with h-index 135. He is in the editorial board on 10 major international journals. More information about his research contribution can be found from https://www.coin-lab.org.
Prof. Deb’s NSGA-II paper for multi-criterion optimization was published in 2002 and has accumulated more than 50,000 Google Scholar citations. NSGA-II has been commercialized by at least three software companies in Europe and USA. He is one of the pioneers of the EMO field and is one of the top-cited researchers in the evolutionary computation field.
He received the prestigious ACM Fellow award in 2023. He has served in the SIGEVO’s executive committee during 2007-2013. Soon after the inception of ACM’s SIGEVO, he received ACM SIGEVO Impact award in 2016. He has been actively involved with SIGEVO’s flagship annual conference entitled ‘Genetic and Evolutionary Computation (GECCO)’ conference as Editor of the proceedings, keynote and tutorial speaker, and track chair. He looks forward to serve as an ACM’s Distinguished Lecturer to share his research and hopefully collaborate with other ACM researchers and young researchers in the near future.
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Machine Learning Assisted Improvements to Multi-Criterion Optimization Algorithms
Multi-criterion optimization problems give rise to a set of Pareto-optimal solutions, which first must be found before a single preferred solution is chosen for implementation. To find a...
- Problem Solving with Multiple Criteria ? A New and Innovative Tool in ComputingMost practical search and optimization related problem-solving tasks involve multiple conflicting criteria, which all must be considered simultaneously during an optimization algorithm. A...
- Recent Advancements in Evolutionary Multi-Criterion Optimization and Decision-making
Evolutionary multi-criterion optimization (EMO) research is now more than three decades old. Efficient algorithms and demonstrative applications have encouraged researchers...
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- Problem Solving with Multiple Criteria ? A New and Innovative Tool in Computing