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Dr. Shunmuga Velayutham C.

Vice Chairperson, School of Computing, Coimbatore | Professor, School of Computing, Coimbatore

Qualification: Ph.D
cs_velayutham@cb.amrita.edu
Dr. Shunmuga Velayutham C.'s Google Scholar Profile
Research Interest: Evolutionary Computing

Bio

Dr. C. Shunmuga Velayutham currently serves as an Vice Chairperson and Professor School of Computing, Coimbatore He has been affiliated with the Department of Computer Science & Engineering since 2005. He received his Ph.D. from Dayalbagh Educational Institute, Agra, Uttar Pradesh in 2005. His Ph.D thesis focussed on the effective design of Neuro-Fuzzy Systems and proposed a novel Asymmetric Subsethood Product Fuzzy Neural Inference System (AsuPFuNIS) with applications in function approximation, classification, prediction, control etc.

His current research interests include Evolutionary Computation specifically, Visualizing Genetic and Evolutionary Computation, Population-algorithm based portfolios, Tuning-free evolutionary algorithms etc. He has supervised two Ph.D.s in the area of Differential Evolution based algorithm portfolios and is currently supervising a Ph.D. student in visual analysis of Differential Evolution search. He is also jointly supervising a Ph.D. student in Tuning-free Differential Evolution.

He has served as a Reviewer for several international journals and conferences including IEEE Transactions on Fuzzy Systems, International Journal of Machine Learning and Cybernetics (Springer), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015),  and IEEE World Congress on Computational Intelligence WCCI 2016 (including Congress on Evolutionary Computation CEC 2016).

Currently he is heading the Evolutionary Computation research group in the Department of Computer Science & Engineering. He has published several peer reviewed papers in International Journals and Conferences.

Publications

Journal Article

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 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 : 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 : 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

Conference Paper

Year : 2019

A Preliminary Investigation on a Graph Model of Differential Evolution Algorithm

Cite this Research Publication : M. T. Indu, Dr. Somasundaram K., and Dr. Shunmuga Velayutham C., “A Preliminary Investigation on a Graph Model of Differential Evolution Algorithm”, in Pattern Recognition and Machine Intelligence, Cham, 2019.

Publisher : Pattern Recognition and Machine Intelligence, Springer International Publishing

Year : 2009

A study on Genetic Algorithm based video abstraction system

Cite this Research Publication : D. K. A. Raju and Dr. Shunmuga Velayutham C., “A study on Genetic Algorithm based video abstraction system”, in 2009 World Congress on Nature Biologically Inspired Computing (NaBIC), 2009.

Publisher : 2009 World Congress on Nature Biologically Inspired Computing (NaBIC)

Year : 2003

Some applications of an asymmetric subsethood product fuzzy neural inference system

Cite this Research Publication : Dr. Shunmuga Velayutham C. and Kumar, S., “Some applications of an asymmetric subsethood product fuzzy neural inference system”, in Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on, 2003.

Publisher : Fuzzy Systems

Conference Proceedings

Year : 2018

Thinker: A Physical Computing Tool Kit for Computational Thinking

Cite this Research Publication : Anantha Narayanan V., Dr. Shyamala C. K., and Dr. Shunmuga Velayutham C., “Thinker: A Physical Computing Tool Kit for Computational Thinking”, 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT). pp. 298-300, 2018.

Publisher : IEEE 18th International Conference on Advanced Learning Technologies (ICALT).

Year : 2017

Teaching computational thinking to entry-level undergraduate engineering students at Amrita

Cite this Research Publication : Dr. Shyamala C. K., Dr. Shunmuga Velayutham C., and Dr. Latha Parameswaran, “Teaching computational thinking to entry-level undergraduate engineering students at Amrita”, IEEE Global Engineering Education Conference, EDUCON. IEEE Computer Society, pp. 1731-1734, 2017.

Publisher : IEEE Global Engineering Education Conference, EDUCON, IEEE Computer Society

Year : 2017

Bayesian nonparametric Multiple Instance Regression

Cite this Research Publication : Dr. Shunmuga Velayutham C., S, S., S, R., S, G., Dr. Bhagavathi Sivakumar P., and S, V., “Bayesian nonparametric Multiple Instance Regression”, Proceedings - International Conference on Pattern Recognition. Institute of Electrical and Electronics Engineers Inc., Cancun, Mexico, pp. 3661-3666, 2017.

Publisher : Institute of Electrical and Electronics Engineers Inc

Year : 2015

Theoretical Analysis of Expected Population Variance Evolution for a Differential Evolution Variant

Cite this Research Publication : Dr. Thangavelu S., Dr. Jeyakumar G., Balakrishnan, R. M., and Dr. Shunmuga Velayutham C., “Theoretical Analysis of Expected Population Variance Evolution for a Differential Evolution Variant”, Computational Intelligence in Data Mining (In Smart Innovation, Systems and Technologies), vol. 32. Smart Innovation, Systems and Technologies, Springer, pp. 403–416, 2015.

Publisher : Computational Intelligence in Data Mining (In Smart Innovation, Systems and Technologies)

Year : 2012

SaddleSURF: A saddle based interest point detector (2012)

Cite this Research Publication : and Dr. Shunmuga Velayutham C., “SaddleSURF: A saddle based interest point detector (2012)”, In Mathematical Modeling and Scientific Computation. Springer, pp. 413-420, 2012.

Publisher : Springer

Year : 2010

A comparative study on theoretical and empirical evolution of population variance of differential evolution variants

Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “A comparative study on theoretical and empirical evolution of population variance of differential evolution variants”, Simulated Evolution and Learning. Springer, pp. 75–79, 2010.

Publisher : Springer

Year : 2010

Analyzing the Explorative power of Differential Evolution Variants on Different Classes of Problems

Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Analyzing the Explorative power of Differential Evolution Variants on Different Classes of Problems”, Lecture Notes in Computer Science (LNCS-6466), Springer-Verlag, vol. 6466. pp. 95-102, 2010.

Publisher : Springer-Verlag

Year : 2010

Empirical study on migration topologies and migration policies for island based distributed differential evolution variants

Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Empirical study on migration topologies and migration policies for island based distributed differential evolution variants”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6466 LNCS, pp. 29-37, 2010.

Publisher : Lecture Notes in Computer Science

Year : 2009

An empirical comparison of differential evolution variants on different classes of unconstrained global optimization problems

Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “An empirical comparison of differential evolution variants on different classes of unconstrained global optimization problems”, World Congress on Nature & Biologically Inspired Computing, NaBIC. IEEE, Coimbatore, pp. 866-871, 2009.

Publisher : World Congress on Nature & Biologically Inspired Computing, NaBIC, IEEE,

Year : 2009

A comparative performance analysis of multiple trial vectors differential evolution and classical differential evolution variants

Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “A comparative performance analysis of multiple trial vectors differential evolution and classical differential evolution variants”, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. Springer, pp. 470–477, 2009

Publisher : Rough Sets, Fuzzy Sets, Data Mining and Granular Computing

Year : 2009

A comparative performance analysis of differential evolution and dynamic differential evolution variants

Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “A comparative performance analysis of differential evolution and dynamic differential evolution variants”, Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, Coimbatore, pp. 464-468, 2009.

Publisher : Nature Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, IEEE

Book Chapter

Year : 2016

Visualization – A Potential Alternative for Analyzing Differential Evolution Search

Cite this Research Publication : P. R. Radhika and Dr. Shunmuga Velayutham C., “Visualization – A Potential Alternative for Analyzing Differential Evolution Search”, in Intelligent Systems Technologies and Applications: Volume 1, S. Berretti, Thampi, S. M., and Srivastava, P. Ranjan, Eds. Cham: Springer International Publishing, 2016, pp. 31–41.

Publisher : Intelligent Systems Technologies and Applications: Volume 1, Springer International Publishing, Cham, p.31–41.

Year : 2016

Hybridizing differential evolution variants through heterogeneous mixing in a distributed framework

Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Hybridizing differential evolution variants through heterogeneous mixing in a distributed framework”, in Studies in Computational Intelligence, vol. 611, New Delhi: Springer, 2016, pp. 107-151.

Publisher : Studies in Computational Intelligence

Year : 2016

Is Differential Evolution Sensitive to Pseudo Random Number Generator Quality? – An Investigation

Cite this Research Publication : L. Rajashekharan and Dr. Shunmuga Velayutham C., “Is Differential Evolution Sensitive to Pseudo Random Number Generator Quality? – An Investigation”, in Intelligent Systems Technologies and Applications: Volume 1, S. Berretti, Thampi, S. M., and Srivastava, P. Ranjan, Eds. Cham: Springer International Publishing, 2016, pp. 305–313.

Publisher : Intelligent Systems Technologies and Applications.

Year : 2011

Experimental Study on Recent Advances in Differential Evolution Algorithm

Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Experimental Study on Recent Advances in Differential Evolution Algorithm”, in Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation, vol. 2, In Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation vol., 2011, pp. 111-133.

Publisher : Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation

Year : 2004

Differential Evolution Based On-Line Feature Analysis in an Asymmetric Subsethood Product Fuzzy Neural Network

Cite this Research Publication : Dr. Shunmuga Velayutham C. and Kumar, S., “Differential Evolution Based On-Line Feature Analysis in an Asymmetric Subsethood Product Fuzzy Neural Network”, in Neural Information Processing: 11th International Conference, ICONIP 2004, Calcutta, India, November 22-25, 2004. Proceedings, N. Ranjan Pal, Kasabov, N., Mudi, R. K., Pal, S., and Parui, S. Kumar, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 959–964.

Publisher : Neural Information Processing: 11th International Conference, ICONIP

Year : 2002

Evolutionary Subsethood Product Fuzzy Neural Network

Cite this Research Publication : Dr. Shunmuga Velayutham C., Paul, S., and Kumar, S., “Evolutionary Subsethood Product Fuzzy Neural Network”, in Advances in Soft Computing –- AFSS 2002: 2002 AFSS International Conference on Fuzzy Systems Calcutta, India, February 3–6, 2002 Proceedings, N. R. Pal and Sugeno, M., Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002, pp. 274–280.

Publisher : Springer Berlin Heidelberg, Berlin, Heidelberg

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