Reprint

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

Edited by
March 2023
292 pages
  • ISBN978-3-0365-6980-2 (Hardback)
  • ISBN978-3-0365-6981-9 (PDF)

This book is a reprint of the Special Issue Evolutionary Multi-objective Optimization: An Honorary Issue Dedicated to Professor Kalyanmoy Deb that was published in

Computer Science & Mathematics
Summary

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.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
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; multi-objective optimization; knowledge discovery; reconfigurable manufacturing system; simulation; evolutionary multi-objective optimization; multi-criteria decision making; interactive optimization; grouping genetic algorithm; grouping mutation operator; grouping problem; unrelated parallel-machine scheduling; multi-objective optimization; hypervolume indicator; newton method; evolutionary algorithms; constraint handling; hypervolume scalarization; association rule mining; causality measures; multi-objective evolutionary algorithm; COVID-19 data; particle filter; multi-objective optimization; transfer learning; objectives reduction; data mining; multi-objective optimization; many objectives; multi-objective reliability-based design optimization; shifting vector approach; reliability analysis; chaos control theory; differential evolution; n/a