Kalyanmoy Deb, Koenig Endowed Chair Professor of Electrical and Computer Engineering and a BEACON member, has crossed 100,000 citation mark according to Google Scholar (https://scholar.google.com/citations?user=paTAXiIAAAAJ&hl=en). He is one of the two faculty members in the College of Engineering, and one among four at MSU to achieve this recognition according to Google Scholar (https://scholar.google.com/citations?hl=en&view_op=search_authors&mauthors=Michigan+State+University). His h-index is 102, indicating 102 of his publications have received at least 102 citations each. For more information about his research in evolutionary computation and optimization, visit http://www.egr.msu.edu/~kdeb or his COIN lab website http://www.coin-laboratory.com.
Kalyan’s and his work have been seen repeatedly on our blog
Pareto Improvement of Pareto-Based Multi-Objective Evolutionary Algorithms (October 5, 2016)
BEACON at GECCO 2016 (August 18, 2016)
BEACON Researchers at Work: Evolution makes software adaptive (March 10, 2014)
BEACON Researchers at Work: What’s a Genetic Algorithm? (October 28, 2013)
Kalyanmoy Deb receives the World Academy of Sciences Prize in Engineering Sciences (October 18, 2013)
BEACON’s Kalyanmoy Deb receives honorary doctorate (September 18, 2013)
BEACON’s Kalyanmoy Deb wins Cajastur Mamdani Prize for Soft Computing (December 8, 2011)
Some of Kalyan’s most cited work includes…
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, volume 6, no. 2, pages 182-197. (23,553 Citations)
This is most likely the highest-cited paper in evolutionary computation. This paper suggested an evolutionary multi-objective optimization (EMO) algorithm, which parameter-less, modular, and computationally fast. NSGA-II boosted the research and application of EMO and is implemented in a number of commercial optimization softwares.
N Srinivas and K Deb. (1995). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, volume 2, no. 3, pages 221-248. (6,003 Citations)
The NSGA procedure proposed in this paper was the precursor of NSGA-II and is one of the three first EMO methods which demonstrated the suitability of evolutionary algorithms for finding multiple Pareto-optimal solutions for a multi-objective optimization problem.
Zitzler, K. Deb, and L Thiele. (1999). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary computation, volume 8, no. 2, pages 173-195. (4,349 Citations).
This paper proposed a six-problem test suite for multi-objective optimization, which are largely known as ZDT problems today. The paper also compared a number of existing EMO methods for solving ZDT test problems. ZDT problems allowed EMO researchers to evaluate their algorithms in a systematic manner.