Qualification: 
Ph.D
cs_velayutham@cb.amrita.edu

Dr. C. Shunmuga Velayutham currently serves as an Associate Professor in the Department of Computer Science & Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham. 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

Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2017

Conference Proceedings

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.[Abstract]


Multiple Instance Regression jointly models a set of instances and its corresponding real-valued output. We present a novel multiple instance regression model that infers a subset of instances in each bag that best describes the bag label and uses them to learn a predictive model in a unified framework. We assume that instances in each bag are drawn from a mixture distribution and thus naturally form groups, and instances from one of this group explain the bag label. The largest cluster is assumed to be correlated with the label. We evaluate this model on the crop yield prediction and aerosol depth prediction problems. The predictive accuracy of our model is better than the state of the art MIR methods.

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2012

Conference Proceedings

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

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

Dr. Shyamala C. K., Dr. Shunmuga Velayutham C., and Dr. Latha Parameswaran, “Teaching computational thinking to entry-level undergraduate engineering students at Amrita”, in IEEE Global Engineering Education Conference, EDUCON, 2017, pp. 1731-1734.[Abstract]


This paper is about various aspects of the Computational Thinking and Problem Solving course offered to entry-level undergraduate engineering students across 7 engineering disciplines at Amrita University, India. The various aspects include the motivations for offering the course, aims and objectives of the course, course design as well as the delivery and assessment of the course. The paper also shares the experience of conducting the course to a very large number of students and the lessons learnt during the process. © 2017 IEEE.

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2009

Conference Paper

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “A comparative performance analysis of differential evolution and dynamic differential evolution variants”, in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, Coimbatore, 2009.[Abstract]


In this paper we present an empirical, comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Dynamic Differential Evolution (DDE) algorithms to solve unconstrained global optimization problems. The aim is to compare DDE, which employs a dynamic evolution mechanism, against DE and to identify the competitive variants which perform reasonably well on problems with different features. The fourteen variants of DE and DDE are benchmarked on 6 test functions grouped by features - unimodal separable, unimodal nonseparable, multimodal separable and multimodal non-separable. The analysis identifies the competitive variants and shows that DDE variants consistently outperform their classical counter parts. More »»

2009

Conference Paper

G. Jeyakumar and Dr. Shunmuga Velayutham C., “An empirical comparison of differential evolution variants on different classes of unconstrained global optimization problems”, in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, Coimbatore, 2009.[Abstract]


This paper presents an empirical analysis of the performance of differential evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken to identify competitive DE variants which perform reasonably well on a range of problems with different features. Towards this, fourteen DE variants were implemented and tested on 14 high dimensional benchmark functions grouped by their modality and decomposability viz., unimodal separable, unimodal nonseparable, multimodal separable and multimodal nonseparable. This extensive performance analysis provides some insight about the competitiveness of DE variants in solving test problems with representative landscape features such as modality and decomposability. More »»

2009

Conference Paper

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.[Abstract]


This paper proposes to investigate on the efficacy of Genetic Algorithm (GA) based video abstraction system to deliver a meaningful summary (still image abstract) with minimal preprocessing on the given video. The GA employs novel crossover and mutation operators to search for a meaningful summary in a search space of all video summaries. This preliminary investigation employs Euclidean and City-block distance measures, based on simple color histogram, color histogram by Gong and color correlogram, among the frames as fitness functions and assume that the number of frames in the still image abstract is known apriori. The performance of GA based video abstraction system has been tested on 6 documentary videos from the open video project. The simulation results, though not very promising, strongly hints the potential of GA for automatic video abstraction and motivates further exploration.

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2003

Conference Paper

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.[Abstract]


This paper presents some applications of an asymmetric subsethood product fuzzy neural inference system (ASuP-FuNIS). The ASuPFuNIS model extends SuPFuNIS by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood product network admits both numeric as well as linguistic inputs. Numeric inputs are fuzzified prior to their application to the network; linguistic inputs are presented without modification. The network architecture directly embeds fuzzy if-then rules, and connections represent antecedent and consequent fuzzy sets. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. The model is economical in terms of the number of rules required to solve difficult problems and is robust against random variations in data sets. Simulation results on three benchmark problems-the Hepatitis diagnosis, Iris data classification and the Narazaki-Ralescu function approximation problem-show that the subsethood based model performs excellently with minimal number of rules.

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Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

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.[Abstract]


The world’s population has been increasing as every year passes by, and Governments across the world face a stupendous challenge of governing each country. These challenges include providing proper sanitation facilities, efficient disaster management techniques, effective resource allocation and management, etc. Crowdsourcing methodologies, which empower the common man to provide valuable information for better decision making, have gained prominence recently to tackle several challenges faced by several governments. In this paper, we introduce a crowdsourcing-based platform that makes use of information provided by the common man for better governance. We illustrate how this platform can be used in several instances to attend to the problems faced by people. © Springer India 2016.

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2016

Journal Article

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Hybridizing differential evolution variants through heterogeneous mixing in a distributed framework”, Studies in Computational Intelligence, vol. 611, pp. 107-151, 2016.[Abstract]


While hybridizing the complementary constituent soft computing techniques has displayed improved efficacy, the hybridization of complementary characteristics of different Differential Evolution (DE) variants (could as well be extended to evolutionary algorithms variants in general) through heterogeneous mixing in a distributed framework also holds a great potential. This chapter proposes to mix competitive DE variants with diverse characteristics in a distributed framework as against the typical distributed (homogeneous) Differential Evolution (dDE) algorithms found in DE literature. After an empirical analysis of 14 classical DE variants on 14 test functions, two heterogeneous dDE frameworks dDE_HeM_best and dDE_HeM_worst obtained by mixing best DE variants and worst DE variants, respectively, have been realized, implemented and tested on the benchmark optimization problems. The simulation results have validated the robustness of the heterogeneous mixing of best variants. The chapter also hybridized DE and dynamic DE variants in a distributed framework. The robustness of the resulting framework has been validated by benchmarking it against the state-of-the-art DE algorithms in the literature.

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2015

Journal Article

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.[Abstract]


Differential Evolution (DE) is a simple but efficient Evolutionary Algorithm (EA) for stochastic real parameter optimization. With various types of mutation and crossover applicable to DE, there exist many variants of DE. The empirical comparisons between the performances of these variants on chosen benchmarking problems are well reported in literature. However, attempts to analyze the reason for such identified behavior of the variants are scarce. As an attempt in this direction, this paper empirically analyzes the performance as well as the reason for such performance of 14 classical DE variants on 4 benchmarking functions with different modality and decomposability. The empirical analysis is carried out by measuring the mean objective function values (MOV), success rate (Sr), probability of convergence (Pc), quality measure (Qm) and empirical evolution of the variance of the population (Evar). The study also includes reporting evidences for the variants suffering with stagnation and/or premature convergence. © 2014 S. Thangavelu, G. Jeyakumar and C. Shunmuga Velyautham.

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2015

Journal Article

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.[Abstract]


This paper attempts a preliminary investigation to gain insight about the cooperative dynamics of mixing the four classical differential evolution (DE) variants viz. DE/rand/1/bin, DE/best/1/bin, DE/rand/2/bin and DE/best/2/bin in an island-based distributed framework. The exhaustive combinations of the above said four DE variants in an island size of 4, resulting in 35 distributed DE variants, have all been implemented and tested on 14 unconstrained test functions with diverse features grouped by their modality and decomposability. Simulation results show that the rand-best variants' mixing, display a better cooperative characteristics than rand-rand and best-best variants' mixing. This insight motivated for further investigations on mixing DE/rand-to-best/1/bin (a variant which intrinsically employs rand and best strategies) with DE/rand/1/bin and DE/best/1/bin in the distributed framework. Simulation results reiterated the observations about the cooperative characteristics of rand-best variants' combinations with the latter mixing showing still better cooperative characteristics both in terms of probability of convergence and convergence rate. Copyright © 2015 Inderscience Enterprises Ltd.

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2015

Journal Article

S. S. Thangavelu 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.[Abstract]


This paper proposes to combine three different Differential Evolution (DE) variants viz. DE/rand/1/bin, DE/best/1/bin and DE/rand-to-best/1/bin in an island based distributed Differential Evolution (dDE) framework. The resulting novel dDEs with different DE variants in each islands have been tested on 13 highdimensional benchmark problems (of dimensions 500 and 1000) to observe their performance efficacy as well as to investigate the potential of combining such complementary collection of search strategies in a distributed framework. Simulation results show that rand and rand-to-best strategy combination variants display superior performance over rand, best, rand-to-best as well as best, rand-to-best combination variants. The rand and best strategy combinations displayed the poor performance. The simulation studies indicate a definite potential of combining complementary collection of search characteristics in an island based distributed framework to realize highly co-operative, efficient and robust distributed Differential Evolution variants capable of handling a wide variety of optimizations tasks. © Springer International Publishing Switzerland 2015.

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2013

Journal Article

Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization”, Soft Computing, vol. 18, pp. 1949-1965, 2013.[Abstract]


This paper proposes a novel distributed differential evolution framework called distributed mixed variants (dynamic) differential evolution ( {Mathematical expression}. This novel framework is a heterogeneous mix of effective differential evolution (DE) and dynamic differential evolution (DDE) variants with diverse characteristics in a distributed framework to result in {Mathematical expression}. The {Mathematical expression}, discussed in this paper, constitute various proportions and combinations of DE/best/2/bin and DDE/best/2/bin as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of {Mathematical expression} as whole. The {Mathematical expression} variants have been run on 14 test problems of 30 dimensions to display their competitive performance over the distributed classical and dynamic versions of the constituent variants. The {Mathematical expression}, when benchmarked on a different 13 test problems of 500 as well as 1,000 dimensions, scaled well and outperformed, on an average, five existing distributed differential evolution algorithms.

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2013

Journal Article

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.[Abstract]


<p>This paper proposes a novel distributed differential evolution algorithm called Distributed Mixed Variant Differential Evolution (dmvDE). To alleviate the time consuming trial-and-error selection of appropriate Differential Evolution (DE) variant to solve a given optimization problem, dmvDE proposes to mix effective DE variants with diverse characteristics in a distributed framework. The novelty of dmvDEs lies in mixing different DE variants in an island based distributed framework. The 19 dmvDE algorithms, discussed in this paper, constitute various proportions and combinations of four DE variants (DE/rand/1/bin, DE/rand/2/bin, DE/best/2/bin and DE/rand-to-best/1/bin) as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of the distributed DE as a whole. The dmvDE algorithms have been run on a set of test problems and compared to the distributed versions of the constituent DE variants. Simulation results show that dmvDEs display a consistent overall improvement in performance than that of distributed DEs. The best of dmvDE algorithms has also been benchmarked against five distributed differential evolution algorithms. Simulation results reiterate the superior performance of the mixing of the DE variants in a distributed frame work. The best of dmvDE algorithms outperforms, on average, all five algorithms considered. © 2013 Springer-Verlag Berlin Heidelberg.</p>

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2012

Journal Article

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.[Abstract]


In this paper, we extend the dynamicity of differential evolution (DE) proposed for DE/rand/1/bin and DE/best/1/bin to five more variants DE/rand/2, DE/best/2, DE/current-to-rand/1, DE/current-to-best/1 and DE/rand-to-best/1. We present an empirical, comparative performance, analysis of 14 variants of DE and dynamic differential evolution (DDE) algorithms (7 variants with two crossovers - binomial and exponential) to solve unconstrained global optimization problems. The aim of this paper is to identify competitive DE and DDE variants which perform well on ifferent problems, and to compare the performance of DDE variants with DE variants. The performance of 14 variants of DE and DDE are analyzed by implementing them on 14 test functions. The analysis (done based on mean objective function value, probability of convergence and success performance) shows the superiority of DDE variants and identifies the competitive DE and DDE variants.

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2010

Journal Article

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.[Abstract]


In this paper we present an empirical performance analysis of fourteen variants of Differential Evolution (DE) on a set of unconstrained global optimization problems. The island based distributed differential evolution counterparts of the above said 14 variants have been implemented with mesh and ring migration topologies and their superior performance over the serial implementation has been demonstrated. The competitive performance of ring topology based distributed differential evolution variants on the chosen problem has also been demonstrated. Six different migration policies are experimented for ring topology, and their performances are reported. © 2010 Springer-Verlag. More »»

2010

Journal Article

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.[Abstract]


In this paper, a parametric study of Generalized Generation Gap (G3) Genetic Algorithm (GA) model with Simplex crossover (SPX) using Taguchi method has been presented. Population size, number of parents and offspring pool size are considered as design factors with five levels. The analysis of mean factor is conducted to find the influence of design factors and their optimal combination for six benchmark functions. The experimental results suggest more experiments on granularity of design factor levels for better performance efficacy. © 2010 Springer-Verlag. More »»

2010

Journal Article

G. Jeyakumar 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.[Abstract]


In this paper we present an empirical, comparative performance, analysis of fourteen Differential Evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken, with an objective, to compare and to identify competitive DE variants which perform reasonably well on problems with different features. Towards this, fourteen variants of DE are benchmarked on 14 high dimensional unconstrained test functions grouped by their modality and decomposability viz. unimodal separable, unimodal nonseparable, multimodal separable and multimodal nonseparable. The analysis identifies the overall competitive variants as well as the feature based performances of all the variants. More »»

2010

Journal Article

G. Jeyakumar 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.

2009

Journal Article

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.

2005

Journal Article

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.[Abstract]


This work presents an asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS) that directly extends the SuPFuNIS model by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood-product network admits both numeric as well as linguistic inputs. Input nodes, which act as tunable feature fuzzifiers, fuzzify numeric inputs with asymmetric Gaussian fuzzy sets; and linguistic inputs are presented as is. The antecedent and consequent labels of standard fuzzy if-then rules are represented as asymmetric Gaussian fuzzy connection weights of the network. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. Despite the increase in the number of free parameters, the proposed model performs better than SuPFuNIS, on various benchmarking problems, both in terms of the performance accuracy and architectural economy and compares excellently with other various existing models with a performance better than most of them. More »»

2003

Journal Article

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.

2002

Journal Article

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.

Publication Type: Book Chapter

Year of Publication Publication Type Title

2016

Book Chapter

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 Cham: Springer International Publishing, 2016, pp. 31–41.[Abstract]


This paper is a preliminary investigation towards employing Visualization as a potential alternative to analyze Differential Evolution search as against the typical theoretical and empirical analyses. The usefulness of scatter plots and difference vector visualization has been observed on six Differential Evolution variants. Simulation analysis reiterated their potential beyond analyzing mere convergence. It has also been observed that scatter plots and difference vector visualization can be employed to detect premature convergence and stagnation. More »»

2016

Book Chapter

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 Cham: Springer International Publishing, 2016, pp. 305–313.[Abstract]


This paper intends to investigate the sensitivity of Differential Evolution (DE) algorithm towards Pseudo Random Number Generator (PRNG) quality. Towards this, the impact of six PRNGs on the performance quality of 14 DE variants in solving nineteen 10-Dimensional benchmark functions has been studied. The results suggest that DE algorithm is insensitive to the quality of PRNG used. More »»

2015

Book Chapter

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


In this paper we derive an analytical expression to describe the evolution of expected population variance for Differential Evolution (DE) variant—DE/current-to-best/1/bin (as a measure of its explorative power). The derived theoretical evolution of population variance has been validated by comparing it against the empirical evolution of population variance by DE/current-to-best/1/bin on four benchmark functions.

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2010

Book Chapter

G. Jeyakumar and Dr. Shunmuga Velayutham C., “A comparative study on theoretical and empirical evolution of population variance of differential evolution variants”, in Simulated Evolution and Learning, Springer, 2010, pp. 75–79.[Abstract]


In this paper we derive theoretical expressions to compute expected population variance for Differential Evolution (DE) variants – DE/best/1/bin, DE/rand/2/bin and DE/best/2/bin by directly extending Zaharie’s work on DE/rand/1/bin. The study includes comparing the theoretical and empirical evolution of population variance of three DE variants. This work provides insight about the explorative power of the variants and explains their behavior. More »»

2009

Book Chapter

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


In this paper we present an empirical , comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Multiple Trial Vectors Differential Evolution algorithms to solve unconstrained global optimization problems. The aim is (1) to compare Multiple Trial Vectors DE, which allows each parent vector in the population to generate more than one trial vector, against the classical DE and (2) to identify the competitive variants which perform reasonably well on problems with different features. The DE and Multiple Trial Vectors DE variants are benchmarked on 6 test functions grouped by features – unimodal separable, unimodal nonseparable, multimodal separable and multimodal non-separable. The analysis identifies the competitive variants and shows that Multiple Trial Vectors DE compares well with the classical DE. More »»

2004

Book Chapter

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 Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 959–964.

2002

Book Chapter

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. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002, pp. 274–280.

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