Year : 2020
A preliminary investigation into automatically evolving computer viruses using evolutionary algorithms
Cite this Research Publication : Ritwik Murali and Dr. Shunmuga Velayutham C., “A preliminary investigation into automatically evolving computer viruses using evolutionary algorithms”, Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6517 - 6526, 2020.
Publisher : Journal of Intelligent & Fuzzy Systems, IOS Press
Year : 2019
Heterogeneous Mixing of Dynamic Differential Evolution Variants in Distributed Frame work for Global Optimisation Problems
Cite this Research Publication : Dr. Shunmuga Velayutham C. and Dr. Jeyakumar G., “Heterogeneous Mixing of Dynamic Differential Evolution Variants in Distributed Frame work for Global Optimisation Problems”, International Journal of Advanced Intelligence Paradigms, vol. 1, p. 1, 2019.
Publisher : International Journal of Advanced Intelligence Paradigms
Year : 2016
A crowdsourcing-based platform for better governance
Cite this Research Publication : C. Vishal, V., R. Shivnesh, Kumar, V. Romil, Anirudh, M., Dr. Bhagavathi Sivakumar P., Dr. Shunmuga Velayutham C., Suresh, L. P., and Panigrahi, B. K., “A crowdsourcing-based platform for better governance”, Proceedings of the International Conference on Soft Computing Systems, Advances in Intelligent Systems and Computing, in L.P. Suresh and B.K. Panigrahi (eds), vol. 397, pp. 519-527, 2016.
Publisher : Springer, New Delhi
Year : 2015
Population variance based empirical analysis of the behavior of differential evolution variants
Cite this Research Publication : Dr. Thangavelu S., Dr. Jeyakumar G., and Dr. Shunmuga Velayutham C., “Population variance based empirical analysis of the behavior of differential evolution variants”, Applied Mathematical Sciences, vol. 9, pp. 3249-3263, 2015.
Publisher : Applied Mathematical Sciences
Year : 2015
An investigation on mixing heterogeneous differential evolution variants in a distributed framework
Cite this Research Publication : Dr. Thangavelu S. and Dr. Shunmuga Velayutham C., “An investigation on mixing heterogeneous differential evolution variants in a distributed framework”, International Journal of Bio-Inspired Computation, vol. 7, pp. 307-320, 2015.
Publisher : International Journal of Bio-Inspired Computation, Inderscience Publishers
Year : 2015
Combining different differential evolution variants in an island based distributed framework–an investigation
Cite this Research Publication : Dr. Thangavelu S. and Dr. Shunmuga Velayutham C., “Combining different differential evolution variants in an island based distributed framework–an investigation”, Advances in Intelligent Systems and Computing, vol. 320, pp. 593-606, 2015.
Publisher : Advances in Intelligent Systems and Computing
Year : 2013
Distributed mixed variant differential evolution algorithms for unconstrained global optimization
Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Distributed mixed variant differential evolution algorithms for unconstrained global optimization”, Memetic Computing, vol. 5, pp. 275-293, 2013.
Publisher : Memetic Computing
Year : 2013
Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization
Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization”, Soft Computing – Springer, vol. 18, pp. 1949-1965, 2014.
Publisher : Soft Computing
Year : 2012
SaddleSURF: A saddle based interest point detector
Cite this Research Publication :
S. S. Kecheril, Issac, A., and C. Velayutham, S., “SaddleSURF: A saddle based interest point detector”, Communications in Computer and Information Science, vol. 283 CCIS, pp. 413-420, 2012.
Publisher : Communications in Computer and Information Science
Year : 2012
Differential evolution and dynamic differential evolution variants – An empirical comparative performance analysis
Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Differential evolution and dynamic differential evolution variants - An empirical comparative performance analysis”, International Journal of Computers and Applications, vol. 34, pp. 135-144, 2012.
Publisher : International Journal of Computers and Applications
Year : 2010
Taguchi method based parametric study of generalized generation gap genetic algorithm model
Cite this Research Publication : Dr. Thangavelu S. and Dr. Shunmuga Velayutham C., “Taguchi method based parametric study of generalized generation gap genetic algorithm model”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6466 LNCS, pp. 344-350, 2010.
Publisher : Lecture Notes in Computer Science
Year : 2010
An empirical performance analysis of differential evolution variants on unconstrained global optimization problems
Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “An empirical performance analysis of differential evolution variants on unconstrained global optimization problems”, International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM), vol. 2, pp. 77–86, 2010.
Publisher : IJCISIM
Year : 2010
Differential Evolution and Dynamic Differential Evolution for High Dimensional Function Optimization – An Empirical Scalability Study
Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Differential Evolution and Dynamic Differential Evolution for High Dimensional Function Optimization – An Empirical Scalability Study”, International Journal of Computer Science and Engineering (IJCSE), vol. 2, pp. 2932-2941, 2010.
Publisher : IJCSE
Year : 2009
Performance and Scalability Analysis of Differential Evolution Variants on a Suite of High Dimensional Benchmark Functions
Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Performance and Scalability Analysis of Differential Evolution Variants on a Suite of High Dimensional Benchmark Functions”, “Mathematical and Computational Models – Recent Trends”, p. Page–No, 2009.
Publisher : “Mathematical and Computational Models – Recent Trends”
Year : 2005
Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS)
Cite this Research Publication : Dr. Shunmuga Velayutham C. and Kumar, S., “Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS)”, IEEE Transactions on Neural Networks, vol. 16, pp. 160-174, 2005.
Publisher : IEEE Transactions on Neural Networks
Year : 2003
A Neuro-Fuzzy Model For Rule Extraction
Cite this Research Publication : G. M. S. Srivastava, Dr. Shunmuga Velayutham C., Paul, S., and Kumar, S., “A Neuro-Fuzzy Model For Rule Extraction”, Advances in Pattern Recognition ICAPR2003, vol. 2, p. 464, 2003.
Publisher : Allied Publishers
Year : 2002
Evolvable subsethood product fuzzy neural network for pattern classification
Cite this Research Publication :
Dr. Shunmuga Velayutham C., Kumar, S., and Paul, S., “Evolvable subsethood product fuzzy neural network for pattern classification”, International journal of pattern recognition and artificial intelligence, vol. 16, pp. 957–970, 2002.
Publisher : World Scientific