Jeyakumar G

Work place: Department of Computer Science and Engineering, Amrita School of Computing, Coimbatore – 641112, India



Research Interests:


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 a Ph.D. degree in Distributed Differential Evolution Algorithm in 2013, from Amrita Vishwa Vidyapeetham University, Tamil Nadu, India. He is currently a Professor in the Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham University, Tamil Nadu, India since 2000.
His research interests include Parallelization and Applications of Evolutionary Algorithms, Artificial Intelligence Techniques, and Human Modeling. He has published numerous papers in reputable journals and conference proceedings, out of which the majority of the publications are indexed in SCOPUS. He has got the best paper awards for a few of his publications. He has guided many students' projects/theses belonging to the courses B. Tech, M. Tech (CSE), M. Tech (Automotive Eng.), M.C.A, and M. Phil and currently guiding Ph.D. scholars, many UG and PG students.

Author Articles
Performance Evaluation of Evolutionary Algorithms on Solving the Influence Maximization Problem in Social Networks

By Agash Uthayasuriyan Hema Chandran G Kavvin UV Sabbineni Hema Mahitha Jeyakumar G

DOI:, Pub. Date: 8 Apr. 2024

Influence Maximization (IM) is an optimization problem that deals with identifying the most valuable individuals/ nodes present in the network to attain the maximal information spread when they are activated. Evolutionary Algorithms (EAs) inspired from nature are one of the most powerful methods to solve an optimization problem. This paper attempts to solve the IM problem using few of the popular EAs such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Differential Evolution (DE). These algorithm’s performance is evaluated using four comparative metrics, that deal with assessing solution quality, computational efficiency, and scalability. The experimental results of these EAs when tested on several real-world networks reveal that the GE and PSO algorithms were found to produce relatively poorer quality of seed nodes and also with higher computational costs, making it less preferrable. DE was able to obtain the best seed sets (in terms of influence spread value) than other algorithms and ACO, the fastest among all the considered algorithms in terms of execution time, was found to obtain seed set with appreciable influence spread with a slightly higher computational cost than DE. 

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