* Research Sites *
Dr. Dipankar Dasgupta
333 Dunn Hall
Memphis, TN 38152-3240
phone: (901) 678-4147
fax: (901) 678-1506


Elevated to IEEE Fellow(Batch of 2015)
Distinguished ACM Speaker
Recipient of 2012 Willard R. Sparks Eminent Faculty Award.

Advisory Board Member of MIT in Cyber Security

Editorial Board of journals


* Principal Investigator *
Dr. Dasgupta will organize symposium on Computational Intelligence in Cyber Security (CICS) at the IEEE Symposium Series on Computational Intelligence (SSCI) in Bengaluru, India from November 18-21, 2018.Click here for more details    Dr. Dasgupta organized IEEE Symposium on Computational Intelligence in Cyber Security (CICS 2017) at Hawaii, USA from November 27-December 1, 2017.     Program Committee Member of the 1st IEEE International Workshop on Cyber Resiliency Economics (CRE 2016) , Vienna, Austria, August 1-3, 2016.    Prof. Dasgupta will give an invited talk at the Computer Science Department, University of Tennessee, Knoxville, TN, April 7, 2016     Prof. Dasgupta will present a research paper at 11th Annual Cyber and Information Security Research (CISR) Conference will be held at the conference center at Oak Ridge National Laboratory, Oak Ridge, TN, April 4 - 6, 2016.     Prof. Dasgupta will give invited talk at Regional Symposium "Graduate Education and Research in Information Security",'GERIS'16, on March 8, 2016, at Binghamton University,Binghamton, New York.     Announcement for the available position in Research Assitant Professor (in Cyber Security)     Prof. Dasgupta was interviewed by a local TV Channel (FOX 13) and telecast on Feb. 19, 2016. Click here for Video.     Organized "Cybersecurity Certificate Course" foundational program at FedEx Institute of Technology,UofM, February 1-5, 2016.     Prof. Dasgupta gave an invited talk on 5th International Conference on Fuzzy and Neural Computing, FANCCO-2015, December 16-19, 2015.     Cluster to Advance Cyber Security & Testing (CAST) hosted Cybersecurity Lightning Talks at the FedEx Institute of Technology, afternoon of December 3, 2015     CfIA Receives Cyber Security Training Grant from FEMA     UofM's CfIA Will Develop Course for Mobile Device Security and Privacy Issues     Prof. Dasgupta gave an invited talk on Adaptive Multi-Factor Authentication at the Department of Electrical Engineering and Computer Science and CASE Center, Syracuse University, Syracuse, NY 13224-5040 November 18, 2015     Organize a Symposium on Computational Intelligence in Cyber Security (CICS) at IEEE Symposium Series on Computational Intelligence (SSCI,), December 7-10, 2015 at Cap Town, South Africa     Gave keynote speech at St. Louis at Cyber Security workshop (STL-CyberCon), University of Missouri-St. Louis, November 20, 2015     Prof. Dasgupta attended the NIST-NICE conference at San Diego from November 1-4, 2015     Prof. Dasgupta gave an invited talk at 9th International Research Workshop on Advances and Innovations in Systems Testing at FedEx Institute of Technology, the University of Memphis, October 20, 2015     Our Cyber Security Team got a second position on Cyber Defense Competition @CANSec 2015, held on 24th October at University of Arkansas at Little Rock

Evolutionary Computation

Book: Evolutionary Algorithms in Engineering Applications

Structured Genetic Algorithms (st. GA)

The field of biological evolution brought a new age in adaptive computation (AC). Among different evolutionary computation approaches, Genetic Algorithms (GA) are receiving much attention both in academic and industries. Genetic algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. Genetic algorithm-based tools have started growing impact in companies - predicting financial market, in factories - job scheduling etc. with their power of search, optimization, adaptation and learning. For the users of diversified fields, genetic algorithms are appealing because of their simplicity, easy to interface and ease to extensibility.

Despite their generally robust character, as the application increases, there found many domains where formal GAs perform poorly. Several modifications have been suggested to alleviate the difficulties both in the manipulation of encoded information and the ways of representing problem spaces. A number of different models, namely, Messy GAs and Genetic Programmings developed recently which addressed the representation issue of GAs.

Dipankar Dasgupta has been involved in the investigation of a more biologically motivated genetic search model - called the Structured Genetic Algorithm (sGA). The model uses some complex mechanisms of biological systems for developing a more efficient genetic search technique. Specifically, this model incorporates redundant genetic material and a gene activation mechanism which utilizes multi-layered genomic structures for the chromosome. The additional genetic material has many advantages in search and optimization. It mainly serves two purposes: primarily, it can maintain genetic diversity at all time during the search process, where the expected variability largely depends on the amount of redundancy incorporated in the encoding.

The following paragraphs summarize some aspects and advantages of Structured Genetic Algorithms:

One school of thought (Darwinian) believes that evolutionary changes are gradual; another (Punctuated Equilibria) postulates that evolutionary changes go in sudden bursts, punctuating long periods of stasis when very small evolutionary changes take place in a given lineage. The new model provides a good framework for carrying out studies that could bridge these two theories.

Structured GA's results to date are very encouraging, though there remain many issues for further investigation. It appears to an enhancement of the formal genetic model with a number of practical advantages. This approach has also received favorable attention in the field of evolutionary computation. However, the studies on structured GAs done so far are only the first step toward the broader goal of developing a more efficient genetic search. Further research to understand the behavior of the model and to determine its search properties is in progress.