Evolutionary Multi-objective Optimization: An Honorary Issue Dedicated to Professor Kalyanmoy Deb

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https://mdpi.com/books/pdfview/book/7055Contributor(s)
Coello, Carlos (editor)
Goodman, Erik (editor)
Miettinen, Kaisa (editor)
Saxena, Dhish (editor)
Schütze, Oliver (editor)
Thiele, Lothar (editor)
Language
EnglishAbstract
This volume is a reprint of the Honorary Special Issue dedicated to the 60th birthday of Professor Dr. Kalyanmoy Deb, published in the journal Mathematical and Computational Applications (MCA). Kalyanmoy Deb has been a pioneer and highly impactful and influential proponent of Evolutionary Multi-objective Optimization (EMO) since 1994. He is currently a Koenig Endowed Chair Professor and University Distinguished Professor in the Department of Electrical and Computer Engineering at Michigan State University, USA, and holds additional appointments in Mechanical Engineering and in Computer Science and Engineering. Professor Deb’s research interests are in evolutionary optimization and its application in multi-objective optimization, modeling, machine learning, and in multi-objective decision making. He has been a visiting professor at various universities across the world, including IITs in India, Aalto University in Finland, the University of Skovde in Sweden, and Nanyang Technological University in Singapore. He was awarded the IEEE Evolutionary Computation Pioneer Award, the Infosys Prize, the TWAS Prize in Engineering Sciences, the CajAstur Mamdani Prize, the Distinguished Alumni Award from IIT Kharagpur, the Edgeworth Pareto Award, the Bhatnagar Prize in Engineering Sciences, and the Bessel Research Award from Germany. He is a fellow of IEEE, ASME, and three Indian science and engineering academies.
Keywords
evolutionary multi-objective optimization; archiving; convergence; multi-objective optimization; genetic algorithm; simple cell mapping; rod vibration; mass–damper–spring termination; impulse response; reliability; importance sampling; scarce data; surrogate; RBDO; MOO; NSGA-II; auto-configuration and auto-design of metaheuristics; large-scale multi-objective optimization; real-world problems optimization; knowledge discovery; reconfigurable manufacturing system; simulation; multi-criteria decision making; interactive optimization; grouping genetic algorithm; grouping mutation operator; grouping problem; unrelated parallel-machine scheduling; hypervolume indicator; newton method; evolutionary algorithms; constraint handling; hypervolume scalarization; association rule mining; causality measures; multi-objective evolutionary algorithm; COVID-19 data; particle filter; transfer learning; objectives reduction; data mining; many objectives; multi-objective reliability-based design optimization; shifting vector approach; reliability analysis; chaos control theory; differential evolution; n/aWebshop link
https://mdpi.com/books/pdfview ...ISBN
9783036569802, 9783036569819Publisher website
www.mdpi.com/booksPublication date and place
Basel, 2023Classification
Information technology industries
Computer science