Qualification: 
Ph.D
Email: 
g_jeyakumar@cb.amrita.edu

Dr G. Jeyakumar received his B.Sc. degree in Mathematics in 1994, M.C.A. degree (under the faculty of Engineering) in 1998 from Bharathidasan University, and Ph.D. degree in Distributed Differential Evolution Algorithm in 2013, from Amrita Vishwa Vidyapeetham, Tamil Nadu, India. He is currently an Associate Professor in the department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Tamil Nadu, India. He is working at Amrita Vishwa Vidyapeetham since 2000.

His research interest includes parallelization and applications of evolutionary algorithms, Artificial Intelligence Techniques and Human Modeling. He has published numerous papers in reputed journals and conference proceedings, out of which majority of the papers are indexed in SCOPUS. He has got best paper awards for his publications. He has guided many student projects/thesis belong to the courses B.Tech., M.Tech., M.C.A. and M.Phil. and currently guiding Ph.D. scholars and many UG and PG students.

Workshops/FDP Conducted

  1. Organized a student workshop on android app development for two years SWAD-2014 and SWAD-2015.
  2. Organized a three days workshop on “Publishing your research” for PG students at Amrita Vishwa Vidyapeetham, on May-2014.

Visits and Talks

  1. Presented a session on “Database Management System Fundamentals” in the orientation program conducted by Amrita Vishwa Vidyapeetham for MCA students, on August – 7 to 11. 2014.
  2. Presented a session on “Publication Based on Research” in the research workshop conducted by Amrita Vishwa Vidyapeetham  on “Publishing your research” , May -26 To 28 2014
  3. Presented a Session on “Artificial Intelligence: Basics to Research”, in the workshop organized by Shri Muthayammal College of Engineering, Rasipuram on 23rd October 2010.
  4. Delivered a Lecture on “Using VC++ for Real Time Application” in the seminar conducted by Sri Narayana Guru College of Arts and Science on 11th Feb ‘2008.
  5. Delivered a Lecture on “Recent Trends in IT World” in the seminar conducted by Sri Narayana Guru College of Arts and Science on 6th Oct ‘2008.
  6. Acted as Faculty(Resource Person) for Infosys Campus Connect Programme during the month of November’2007
  7. Delivered a Lecture on “Developing Programming Skill” in the seminar conducted by Sankara College of Science and Commerce on 22nd  July 2006.
  8. Delivered a Lecture on “Role of Parents in Children’s Education” in the special seminar conducted by The Central Board for Workers Education (Ministry of Labour and Employment, Government of India) for unorganized laborers at Navakarai on 8th and 9th of July 2004.
  9. Contributed as Resource person for the AICTE-ISTE  sponsored STTP on “Information  Retrieval And  Security” organized during November’22 – December’3 2004 by the Department of CSE, Amrita Vishwa Peetham.
  10.  Contributed as Resource person for the AICTE-ISTE sponsored STTP on “Wavelets for Computer Graphics” organized during Jan 21 – 30 ’2003 by the Department of CSE, Amrita Vishwa Peetham.

Awards & Honors

  1. Got university third rank and Gold medal for MCA (1995 -1998) from Bharathidasan University, Trichy.
  2. Got Best Staff'2000 award from my previous working place "Girvani Degree College", Chittoor, Andra Pradesh.
  3. Reviewer for journals :International Journal of Computer Science Issues (IJCSI), International Journal of Swarm and Evolutionary Computation (SWEVO), International Journal of Machine Learning Cybernetics, International Journal of Soft computing, IGI Global
  4. Reviewer for conferences : CNSA 2011, NECOM 2011, WEST 2011, WiMON 2011, AICTY 2011,  AICWIC2013,  ICCCI2014,  ICES2014, ICONIAAC'14, ICCIDM'2014, Smart Grid2015,
  5. Editor for Journals: International Journal of Computer Science and Programming Language, International Journal of Algorithms Design and Analysis and International Journal of Distributed Computing and Technology,
  6. Technical Committee member for ICCA 2012 (International Conference on Computer Applications), Pondicherry.
  7. Acted as Judge for Technical Paper Presentation – Techbash’11 Conducted by Department of MCA, Sri Shakthi Institute of Engineering and Technology, 16/09/11.
  8. Acted as expert committee member for staff selection, for “Sri Sakthi Institute of Technology”, Coimbatore.
  9. Appointed as observer for All India Engineering/Architecture Entrance Examination (AIEEE) - 2007 to 2012.
  10. Got best paper awards in the international conferences IAMA 2009, and ICSCS-2016.
  11. Chaired a Session: "International Conference on Computer Science and Information Technology” (CCSIT 2010), Bangalore, India, on Jan 02-04, 2011.

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

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.

More »»

2016

Journal Article

R. Raghu and Dr. Jeyakumar G., “Mathematical Modelling of Migration Process to Measure Population Diversity of Distributed Evolutionary Algorithms”, Indian Journal of Science and Technology, vol. 9, no. 31, 2016.[Abstract]


Background/Objectives: Evolutionary Algorithms (EAs) have a major role in solving optimization problems. Distributed Evolutionary Algorithms (dEAs) improve the performance of classical EAs. In dEAs, the initial population is divided into a number of subpopulations and an independent as well as cooperative coevolution happens among the subpopulations. Methods/Statistical Analysis: The success of dEAs is mainly attributed to the migration process they follow, during the evolution. The migration process alters the diversity of the subpopulations. The contribution of the migration process over the success of dEAs can be better understood and/or improved in the light of changes it brings in the diversity of subpopulations. Three methodologies used in the modelling process are the theoretical approach, statistical approach and the empirical approach. Findings: This paper is to analyze and design a mathematical model of the migration process, for its better understanding. A statistical equation to measure the diversity changes in the subpopulation during the migration process is also derived. The derived equation is validated on different types of populations. Application/Improvement: The derived equation can be applied to study and improve the performance of distributed evolutionary algorithms. More »»

2016

Journal Article

Dhanya M. Dhanalakshmy, Pranav, P., and Dr. Jeyakumar G., “A Survey on Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm”, International Journal on Advanced Science, Engineering and Information Technology (IJASEIT) (Scopus) , vol. 6, no. 5, pp. 613–623, 2016.[Abstract]


Differential Evolution (DE), the well-known optimization algorithm, is a tool under the roof of Evolutionary Algorithms (EAs) for solving non-linear and non-differential optimization problems. DE has many qualities in its hand, which are attributing to its popularity. DE also is known for its simplicity in solving the given problem with few control parameters: the population size (NP), the mutation rate (F) and the crossover rate (Cr). To avoid the difficulty involved in setting of suitable values for NP, F and Cr many parameter adaptation strategies are proposed in the literature. This paper is to present the working principle of the parameter adaptation strategies of F and Cr. The adaptation strategies are categorized based on the logic used by the authors, and clear insights about all the categories are presented. More »»

2015

Journal Article

M. Sanu and Dr. Jeyakumar G., “Empirical performance analysis of distributed differential evolution for varying migration topologies”, International Journal of Applied Engineering Research, vol. 10, pp. 11919-11932, 2015.[Abstract]


Distributed Differential Evolution, advancement in Differential Evolution (DE) algorithm, is based on the principle of cooperation and co-evolution. It provides multiple search space perspectives, alternate search paths and a more balanced exploitation and exploration capabilities to DE. Altogether it improves solution quality and prevents premature convergence and stagnation to a greater extent, compared to serial DE. The solution quality of Distributed DE is based on the choice of migration parameters. One of the most influencing migration parameter is the topology used. The aim of this work is to empirically analyze the performance of Distributed DE for varying migration topologies. Migration topologies differ from each other on the basis of degree and interconnectivity of nodes. This paper empirically analyzes the performance difference of distributed differential evolution algorithm with varying migration topologies, on a set of benchmarking problems. The migration topologies used in our experiments are basic ring and its variants, star, cartwheel, torus and mesh. Experimental results have shown that no single topology can said to be good for all optimization problems. It depends on the complexity and type of objective function to be optimized. Experimental analyses have also exposed the influence of DE variant, employed in different islands, on the performance ordering of topologies. The influence of other parameters like the selection policy and replacement policy are also found to be crucial. © Research India Publications.

More »»

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.

More »»

2015

Journal Article

K. .Roshini, .Bavya, B., and Dr. Jeyakumar G., “RoBaJe – A Simulated Computational Model for Human Memory to Illustrate Encoding and Decoding of Information”, International Journal of Applied Engineering Research, Research India Publications, vol. 9, pp. 26957-26970, 2015.

2015

Journal Article

R. R. Reddy and Dr. Jeyakumar G., “Differential evolution with added components for early detection and avoidance of premature convergence in solving unconstrained global optimization problems”, International Journal of Applied Engineering Research, vol. 10, no. 5, pp. 13579-13594, 2015.[Abstract]


Differential Evolution (DE) is a recent addition to the repository of Evolutionary Algorithms (EAs) under Evolutionary Computing Techniques. As similar to other EAs, DE also used for optimization based on population of members. During population optimization the classical DE faces major problem of premature convergence, which causes the sample members to converge early to a local optimum though there is a global optimum in the search space. This paper presents methods to reduce the effect premature convergence and achieve better optimal solutions. There are few works in the literature for same reason by altering the control parameters of DE viz., scaling factor (F) and crossover rate (CR). However, we propose methods to make suitable amendments in the population level directly after detecting premature convergence during the search of DE. These methods can be added as an additional component to DE algorithm. The proposed components are to replace the population members with distinct highest objective function values with random members, to replace the population members with distinct lowest objective function values with random members, to replace members in random fashion and to increase the population size dynamically to counter the early convergence of the population. The above mentioned techniques are implemented and added with classical DE. The performance efficacy of DE with added components is verified on implementing it over a set of Benchmarking Function Suite with functions of different characteristics. The experimental results proved that DE with above components added is able to achieve better optimum values than the classical DE. More »»

2015

Journal Article

M. S. Akhila, Vidhya, C. R., and Dr. Jeyakumar G., “Population diversity measurement methods to analyze the behavior of differential evolution algorithm”, International Journal of Control Theory and Applications, vol. 8, pp. 1709-1717, 2015.[Abstract]


Differential Evolution (DE), the real parameter optimization algorithm for population based optimization problem, has proved its superiority over variety benchmarking and real time problems. Measuring and visualizing the changes in the diversity of DE population during its search is one of the ways to understand the algorithmic behavior of DE. This helps to provide better insight for proper tuning of control parameters of DE. Hence, an extensive study to describe various possible ways to measure the population diversity of DE algorithm would be a useful tool for the researchers and practitioners of DE. Towards this research direction, this paper presents variety of population diversity measurement methods available for population based algorithm (in general). As well as, as an initial attempt, three methods out of all the identified methods are implemented for DE/rand/1/bin algorithm for a benchmarking function suite with four different functions. The results recorded are presented and discussed in this paper. More »»

2015

Journal Article

R. Raghu and Dr. Jeyakumar G., “Empirical analysis on the population diversity of the sub-population in distributed differential evolution algorithm”, International Journal of Control Theory and Applications, vol. 8, pp. 1809-1816, 2015.[Abstract]


The Distributed Differential Evolution (dDE) algorithm is a natural extension of the Differential Evolution (DE) algorithm, which is a recent addition to the Evolutionary Algorithms (EAs) pool, in the Evolutionary Computing (EC) field of computer science. The algorithmic novelty of the dDE algorithm is well evident in the literature. However, the theoretical studies on the performance of the dDE algorithms are scarcely reported. This paper is an attempt to analyze the performance of the dDE algorithm with a theoretical study. A theoretical equation, to measure the population diversity of the sub-population of the dDE algorithm, after migration, is derived and the validity of the same is verified with a simple distributed framework of dDE with two sub-population. More »»

2014

Journal Article

G. K. Deivanayagam, Gayathiri, D., Manikandan, A., Karthik, K. R. Raghul, Dr. Jeyakumar G., and Kriti, N., “Learning to identify bad coding practice”, International Journal of Applied Engineering Research, vol. 9, pp. 6747-6755, 2014.[Abstract]


Conventional code evaluation systems focus on output matching, with little importance being given to evaluating programming style and practice. However, judgement of coding practice is vital to aid the process of learning how to program. We hence propose to define a framework that evaluates source code by judging good coding practice, rather than by matching the output with predetermined test cases. Since the scope of the problem is large, we plan to implement it for a particular programming platform and paradigm. Our proposed approach is to use well established code metrics in order to evaluate training data, which can be fed to a supervised learning framework. The major challenge that we have identified so far is to come up with a definition of parameters that indicates 'good' coding style. We plan to resolve this issue by training our framework to learn to recognize the optimal values and combination of code metrics in order to comprehensively evaluate coding style. © Research India Publications.

More »»

2014

Journal Article

K. Roshini, Bavya, B., and Dr. Jeyakumar G., “RoBaJe–A Simulated Computational Model for Human Memory to Illustrate Encoding and Decoding of Information”, International Journal of Applied Engineering Research, vol. 9, pp. 26957–26970, 2014.[Abstract]


Modeling a human brain plays a vital part in the simulation of human being behavior. To model human behavior we need to understand the thought process of individuals. Thought process is influenced by the factors that affect the permanent storage of memory. Objective of this paper is to present the details of the experiments to create a simulation of human brain, in particular the storage and retrieval of memory with visual perceptions. It is critical to understand how the human memory works, without a suitable More »»

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.

More »»

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>

More »»

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.

More »»

2012

Journal Article

Dr. Jeyakumar G. and ShunmugaVelayutham, C., “Differential evolution and dynamic differential evolution variants for unconstrained global optimization – An Empirical Comparative Study”, International Journal of Computers and Applications (IJCA), vol. 34, pp. 135-144, 2012.

2011

Journal Article

Dr. Jeyakumar G. and Shanmugavelayutham, C., “Convergence Analysis of Differential Evolution Variants on Unconstrained Global Optimization Functions”, Arxiv preprint arXiv:1105.1901, 2011.

2011

Journal Article

J. Vasudha, Iyshwarya, G., A Selvi, T., Iniyaa, S., and Dr. Jeyakumar G., “Application of Computer-Aided Music Composition in Music Therapy”, International Journal of Innovation, Management and Technology, vol. 2, pp. 55–57, 2011.[Abstract]


Music Therapy is the use of a selected music to obtain the same expected changes and hormonal alterations in the body, played uninterrupted for a while, to obtain the desired positive effect. In this project we try to implement computerized composition of Carnatic Music for curing the ailments. There is a growing awareness that ragas could complement or even be a safe alternative for many medical interventions. For this purpose, it is necessary to design a system which can generate music given the user needs and specifications. Our project aims at implementing this idea by giving the swaras of raagas as input and generating pleasant music using genetic algorithm. This application can be used by the medical practitioners by selecting a raga for playing after giving the patient details and disease as input. Formulating the fitness criteria is a herculean task in order to satisfy coherency, variety, harmony, rhythm and to reduce redundancy. The history of the therapy should also be stored which can be used as a constraint for fitness evaluation. This application is developed using Java. A Java API, called JFugue is used to support music programming. More »»

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. Jeyakumar G. and ShunmugaVelayutham, C., “An Empirical Comparative Performance Analysis of Differential Evolution, Distributed and Mixed-Variants Distributed Differential Evolution Variants”, International Journal of Computational Intelligence Research, vol. 6, pp. 735–742, 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.

Publication Type: Conference Paper

Year of Publication Publication Type Title

2016

Conference Paper

V. Seshadri, Sudheesh, P., Dr. Jeyakumar G., and Dr. Jayakumar M., “Tracking the variation of tidal stature using Kalman filter”, in Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2016, 2016.[Abstract]


The intent of this paper is to track the height of a tidal wave, using the Kalman filter. By using the Kalman filter algorithm, mathematical expressions are derived to determine the height of a tidal wave. By placing buoy sensors at specific locations in the sea, the real tidal wave height is measured. The buoy sensor is placed at a particular distance from the shore. The sensors continuously record data at that particular position at different time intervals and then transmit the data to the receiver on the shoreline. By continuously evaluating this data, the height of the next wave is being estimated. Since a buoy cannot be placed at every point of the wave, this method provides an easy estimation of replicating the process. These sensors are used to simulate the proposed method of tracking the height of a tidal wave and hence giving a warning in advance in case of a wave height which is more than normal. This warning helps people living in coastal areas to vacate the place in advance, therefore avoiding fatality. This tracking of the tidal wave height is useful particularly in the case of a tsunami. By adding Gaussian white noise to the input data from the buoy sensors, a prediction of the next wave height is possible. © 2016 IEEE.

More »»

2016

Conference Paper

K. Sreenath and Dr. Jeyakumar G., “Evolutionary algorithm based rule(s) generation for personalized courseware construction in educational data mining”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India, 2016.[Abstract]


Evolutionary Computing for Educational Data Mining is a research field which with the applications of Evolutionary Algorithms (EAs) to mine, analyze and modify educational data. This paper presents the most relevant studies conducted in this research area. The paper also describes different EAs used for implementing different data mining techniques. It goes on to list how these algorithms are utilized by different educational users to carry out different tasks. Finally, a new combination of EA, Educational User and data mining technique is suggested for implementation. As a part of that a personalized courseware construction technique is proposed and a sample courseware is constructed using the proposed technique. The details about the rule construction and the data mining process involved in the courseware construction techniques are also explained. More »»

2015

Conference Paper

and Dr. Jeyakumar G., “Control Parameter Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm – An Insight”, in In Proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC-2015), , 2015.

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 »»

Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2016

Conference Proceedings

Dr. Jeyakumar G., K, R., N, P., D, K., and B, A., “A Prototype for Student Learning Style Modelling Using Felder-Silverman Learning Style Model”, International Conference on Smart Structures & Systems (ICSSS-2016). Chennai, India, pp. 178 – 182, 2016.

2015

Conference Proceedings

S. Nambiar and Dr. Jeyakumar G., “Co-operative Co-evolution Based Hybridization of Differential Evolution and Particle Swarm Optimization Algorithms in Distributed Environment”, Emerging Research in Computing, Information, Communication and Applications: ERCICA 2015, vol. 2. Springer India, New Delhi, pp. 175–187, 2015.[Abstract]


Evolutionary computing algorithms play a great role in solving real time optimization problems. One of the evolutionary computing algorithm is Particle Swarm Optimization algorithm (PSO). The aim of this paper is to propose a model to improve the performance of PSO algorithm. Hybrid models of Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) has already proved to be one of the better approaches for solving real world complex, dynamic and multimodal optimization problems. But these models hybridize PSO and DE to form a new serial algorithm. In these serial hybridization models, we are losing the originality of both DE and PSO algorithms since the structure of both the algorithms is being modified to get the hybridized PSO and DE algorithm. In this paper, we develop a model for PSO in distributed environment with improved performance in terms of speed and accuracy. The proposed model is a hybridized distributed mixing of DE and PSO (dm-DEPSO) which improves the performance of PSO algorithm. In this model, algorithms are implemented in a cluster environment to perform co-operative co-evolution. Better solutions are migrated from one node to another in the cluster environment. Co-operative co-evolving model shows better performance in terms of speed and accuracy. The algorithm is applied to a set of eight benchmarking functions and their performance are compared by mean of objective function values, standard deviation of objective function values, success rate, probability of convergence and quality measure.

More »»

Publication Type: Book

Year of Publication Publication Type Title

2016

Book

Dr. Shriram K Vasudevan, Subashri, V., Dr. Jeyakumar G., and Prashant R. Nair, Software Engineering. 2016.[Abstract]


Software Engineering provides a comprehensive and exhaustive coverage of the software engineering paradigm and concepts. Various software development life cycle activities like analysis, design and testing are described in detail. An overview of the software product and process is provided with special focus on latest developments like agile methods. Umbrella activities like software configuration management, risk management and change management are elucidated. The book also provides new insights into Process frameworks like CMMI and ISO. Course material for software testing certification is yet another highlight. More »»

Publication Type: Book Chapter

Year of Publication Publication Type Title

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.

More »»

Publication Type: Journal

Year of Publication Publication Type Title

2011

Journal

J. Vasudha, Iniya, S., Iyshwarya, G., and Dr. Jeyakumar G., “Computer Aided Music Generation Using Genetic Algorithm and Its Potential Applications ”. 2011.[Abstract]


Music speaks what cannot be expressed, soothes the mind and gives it rest, heals the heart and makes is whole. Not everyone is gifted with a good voice but almost everyone has good ears when it comes to pleasant music.This music ranges from strictly organized compositions, through improvisational music to aleatoric forms which makes it tough to automate music generation. In this paper we try to bring in the idea of automated Carnatic music generation using genetic algorithm and its applications. Firstly a variety of compositions on specific ragas can be generated which can be improvised to deliver a good quality musical concert. This concept of automated music generation can also be applied to build an ‘online musical instrument tutorials’ by which users get to learn any instrument level by level. The work can also be modified to produce music of same fitness as any pre existing composition by giving the latter’s notes as input. The application of computerized composition can be used in Music Therapy which is the use of a selected music to obtain the same expected changes and hormonal alterations in the body,played uninterrupted for a while, to obtain the desired positive effect. The above mentioned field of music generation can be used by the medical practitioners by selecting a raga for playing after giving the patient details and disease as input. The application is developed using Java. A Java API, called JFugue is used to support music programming. More »»
Faculty Research Interest: 
207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
9th
RANK(INDIA):
NIRF 2017
150+
INTERNATIONAL
PARTNERS