Biologically Inspired Computing Research Group
The Biologically Inspired Computing (BIC) research group at K-State is involved in theoretical and applied research in evolutionary algorithms, ant colony optimization, particle swarm optimization, artificial immune systems, memetic algorithms and neural networks for multi-objective and constrained optimization, prediction, structure discovery, learning and other tasks. Our funded research is in in the areas of plant gene regulatory network modeling and power distribution systems. We are also interested in other applications (communications & networks, engineering, computer science, biology & finance).
Sanjoy Das, Ph.D.
Prof. Das completed his Ph.D. in Electrical & Computer Engineering from Louisiana State University in 1994, and received postdoctoral training at the University of California, Berkeley. He worked for several years in the industry before joining Kansas State University in 2001. Prof. Das is primarily interested in biologically inspired computing, a field of study within artificial intelligence that borrows algorithms from various natural paradigms, such as Darwinian evolution, swarm intelligence, immune systems and neuronal structures to address problems that are too complex to be solved through conventional means. He is investigating the applications of these algorithms for specific optimization problems in plant genomics in collaboration with Prof. Welch and in distribution systems modeling in collaboration with Prof. Pahwa.
|Praveen Koduru, Ph.D.|
Postdoctoral Research Scientist
Dr. Koduru received a B. Tech. in Chemical Engineering from Osmania University, India, an M.S. in Control Systems Engineering from West Virginia University Institute of Technology under the guidance of Prof. Asad Davari, and an M.S. in Electrical Engineering from Yale University under the guidance of Prof. Kumpati S. Narendra. He completed his Ph.D. in Electrical & Computer Engineering from Kansas State University in 2006 under the guidance of Prof. Sanjoy Das and Prof. Stephen M. Welch. Dr. Koduru's research interests are in evolutionary algorithms, genomics, control systems, and artificial intelligence.
Mr. Li received his B. Eng. from Xi'an University of Technology, China, and M. Eng. from Shanghai University, China, in 2001 and 2004 respectively. From 2004 to 2005, he worked as a full-time research assistant in the Department of Electrical Engineering at Hong Kong Polytechnic University. From 2005 to 2006, he worked as a power system engineer in the Hygrand Electronic Equipment Company, China. Mr. Li's current research interests mainly focus on computational intelligence applications and power system control. At present he is working with Dr. Das in evolutionary algorithms.
Xinye Cai received a Bachelor degree in Electronic and Information Engineering from Huazhong University of Science and Technology in Wuhan, China and an M.S in Electronic Engineering from Bio-inspired architecture group, University of York. Currently he is a Ph.D. student under the guidance of Prof. Sanjoy Das and Prof. Stephen M. Welch. His main interest is in evolutionary computation and other bio-inspired algorithms, modeling and simulation, engineering design and evolvable hardware.
Gui Min received a B.S. in Electrical Engineering from Central South University, China, an M.S. in Electrical Engineering from Central South University, China, under the guidance of Prof.Luo An. She currently is a graduate student in Electrical & Computer Engineering from Kansas State University under the guidance of Prof. Anil Pahwa and Prof. Sanjoy Das. Gui Min's research interests are in power system reliability and artificial intelligence.
Assistant Professor, K-State Salina,
Electrical & Computer Engineering
Tel: (785) xxx-xxxx
Email: (in reverse)edu.ksu@eplett
Eduard Plett received an MS in Electrical Engineering from KSU in 2006.
Former advisees/co-advisees (in alphabetic order)
- Kai Ma
- Ashish Ahuja
- Dan Stevens
- Grant Cochenour
- Rajiv Annaluru
- Surasish Nag
- Yujia Zhou
Stephen M. Welch, Ph.D.
Anil Pahwa, Ph.D.
Other major collaborators (in alphabetic order)
- Bala Natarajan, Assistant Professor, Electrical & Computer Engineering
- Gurdip Singh, Professor, Computer & Information Sciences
- Judy Roe, Agronomy (currently in UK)
- Mary Knapp, State Climatologist
- William H. Hsu, Associate Professor, Computer & Information Sciences
(The above is only a partial list of collaborators who have authored multiple papers, obtained funded research, and/or guided students in BIC related fields. There are a few other noteworthy collaborators also not listed here.)
- Multi-objective hybrid evolutionary algorithm with Nelder-Mead based local search
- Parameter estimation of differential equation models of gene regulatory networks
- Reduced complexity particle swarm hybrid algorithm with local search for multi-objective/constrained optimization
- Structure discovery of gene regulatory networks using genetic programming and ant colony algorithms
- Overhead distribution system failure rate prediction using radial basis function networks and wavelet decomposition
- Overhead distribution system anomaly detection using the negative selection algorithm
- Distribution system reconfiguration using multi-objective ant colony optimization and evolutionary strategies
- Multi-objective DS-CDMA code design using the clonal selection principle
Fuzzy Simplex Genetic Algorithm (FSGA)
FSGA is a general purpose hybrid algorithm for fast multi-objective optimization that was developed by Praveen Koduru, Sanjoy Das and Stephen M. Welch that has outperformed all major multi-objective evolutionary algorithms
- Matlab implementation
- C implementation
- Related documents
- Ant Colony Optimization, Belgium (Marco Dorigo)
- Artificial Immune Systems (Dipankar Dasgupta)
- Evoweb, France
- Genetic Programming (John Koza)
- International Conference on Artificial Immune Systems (ICARIS)
- International Society for Artificial Life
- Kanpur Genetic Algorithm Laboratory (KanGAL), India (Kalyanmoy Deb)
- Network for Artificial Immune Systems (ARTIST), UK
- Neural Network Resources
- Particle Swarm Central
- Particle Swarm Optimization (Russ Eberhart)
- Special Interest Group on Evolutionary Computation (ACM SIGEVO)