Recognition to Prof. Kalyanmoy Deb and his Students at Computational Optimization and Innovation (COIN) Laboratory

In the recently concluded annual conference on Genetic and Evolutionary Computation (GECCO) conference at Denver during 20-24 July 2016, Prof. Deb and his graduate students at Computational Optimization and Innovation (COIN) Laboratory received following awards:

Winner of the Niching Methods for Multi-modal Optimization Competition Award:

Benchmarking Covariance Matrix Self Adaption Evolution Strategy with Repelling Subpopulations by Ali Ahrari (ME PhD student, Michigan State University), Kalyanmoy Deb (Michigan State University), and Mike Preuss (University of Munster, Germany. This paper suggests an efficient evolutionary optimization algorithm for finding multiple optimal solutions in a single simulation.

Best Paper Award of Genetic Algorithms track voted by the GECCO participants (http://gecco-2016.sigevo.org/index.html/Best+Paper+Awards):

Breaking the Billion Variable Barrier in Real-World Optimization Using a Customized Evolutionary Algorithm by Kalyanmoy Deb (Michigan State University) and Christie Myburgh (MapTek).  In this paper, Deb and Myburgh developed a population-based evolutionary optimization algorithm to solve a scalable industrial resource allocation problem ranging from 50,000 variables to a staggering one billion variables for the first time. The paper clearly demonstrates the niche of population-based optimization methods vis-à-vis point-based methods in solving large-scale resource allocation problems in practice.

ACM’s SIGEVO Impact Award to Kalyanmoy Deb and his former student J. Sundar for the following 2006 paper published in GECCO-2006 proceedings having highest impact in the past 10 years (http://sig.sigevo.org/index.html/tiki-index.php?page=SIGEVO+Impact+Award):

Reference point based multi-objective optimization using evolutionary algorithms by K. Deb (Michigan State University) and J. Sundar, In: Mike Catolico (Eds), Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp 635-642, ACM, 2006. This paper suggested a reference point based NSGA-II procedure for solving multi-objective optimization problems with preference information, which has accumulated more than 450 Google Scholar citations.

Two other papers from COIN Lab were also nominated for the Best Paper Award of two other tracks at GECCO-2016 conference (http://gecco-2016.sigevo.org/index.html/Best+Paper+Nominations):

Real-World Applications (RWA) Track: 
Finding Reliable Solutions in Bilevel Optimization Problems Under Uncertainties Zhichao Lu (ECE PhD student, Michigan State University), Kalyanmoy Deb (Michigan State University), and Ankur Sinha (Indian Institute of Management, Ahmedabad, India). This paper suggests an optimization algorithm for handling uncertainties in both upper and lower variables for bilevel problems.  

Evolutionary Multi-objective Optimization Track: 
A Generative Kriging Surrogate Model for Constrained and Unconstrained Multi-objective Optimization by Rayan Zakaria Hussein (ECE PhD student, Michigan State University) and Kalyanmoy Deb (Michigan State University). This paper suggests a meta-modeling based optimization algorithm for solving computationally expensive multi-objective problems.

More information about the research activities of COIN lab can be found at www.coin-laboratory.com. Prof. Deb and his students are affiliated with NSF’s BEACON Center for the study of evolution in action and greatly appreciate the support from the center.