Prof. Rajender Singh
Malwa Institute of Technology & Management, Gwalior, India
Professor (Dr.) Rajender Singh Chhillar is a
professor at the Department of Computer Science, Maharshi Dayanand
University, Rohtak, Haryana. He has been teaching in the fields of
computer science and engineering since 1987 and is one of the
founder members of the Department. He obtained his master's degree
from Kurukshetra University, Kurukshetra and Ph.D in computer
science from Maharshi Dayanand University, Rohtak, Haryana. He
received his master of business administration (MBA) degree from
Sikkim Manipal University.
He has visited many countries including France, Hong Kong, China, U.K, Dubai and Nepal. He also won the best paper award in International Conference ICCEE- 2013 held in Paris, France during October 12-13, 2013 and also chaired a session in this conference. He has taught a wide variety of computer courses at University Teaching Departments including software engineering, data structures, data base management system, software testing and quality assurance, software quality management, programming languages, and software design. Professor Chhillar is a director of board, CMAI Asia Association, New Delhi and senior member of IACSIT, Singapore and a member of Computer Society of India. Professor Chhillar has been serving as an editorial board member, guest editor and a reviewer of multiple international journals, and serving as a program committee chair, keynote speaker and session chair of multiple international conferences. He also performs advisory work to Government agencies and academic bodies.
His research interests include software engineering, software testing, software metrics, web metrics, bio metrics, data warehouse and data mining, computer networking, and software design. He has published more than 100 journal and 65 conference papers over the last several years and has also written two books in the fields of software engineering and information technology.
Speech Title: Optimized Test Data Generation Using UML Diagrams and Genetic Algorithm
Abstract: The importance of software testing and its optimization has led to design and development of new software testing techniques for all software development paradigms. Controlling and minimizing software complexity is the most important objective of each software development paradigm because it affects all other software quality attributes like reusability, reliability, testability, maintainability etc. For this purpose, a number of software testing techniques have been designed to report different types of faults based on complexity. The keynote speech focuses on a new testing technique which generates test data for a software by using UML diagrams and genetic algorithm. Test data is generated by fusion of three UML diagrams namely, Activity diagram, sequence diagram and Use-Case diagram. Genetic algorithm is used to optimize the test cases/data. In this way, this technique generates optimized and better test data/cases based on wider coverage of software. By using this, we can improve the quality of a software and can use testing resources more effectively. Therefore, this technique helps in development of good quality software and may be very useful for software industry.
Prof. Kwong Tak Wu Sam
Department of Computer Science, City University of Hong Kong, Hong Kong
Sam Kwong received the B.Sc. degree from the State University of New York at Buffalo, Buffalo, NY, in 1983, the M.A.Sc. degree in electrical engineering from the University of Waterloo, Waterloo, ON, Canada, in 1985, and the Ph.D. degree from the Fernuniversität Hagen, Hagen, Germany, in 1996. From 1985 to 1987, he was a Diagnostic Engineer with Control Data Canada, where he designed the diagnostic software to detect the manufacture faults of the VLSI chips in the Cyber 430 machine. Currently, he is the Associate Editor for the IEEE Transactions On Industrial Informatics, the IEEE Transactions on Industrial Electronics, the Journal of Information Science. He is the Head and Professor of the department of Computer Science, City University of Hong Kong. Prof. Kwong was elevated to IEEE fellow for his contributions on Optimization Techniques for Cybernetics and Video coding in 2014.
Speech Title: Stable Matching-Based Selection in Evolutionary Multiobjective Optimization
Abstract: Multiobjective problems are always aroused in our daily life in that we have to make decisions based on many different objectives. Recently, Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a set of scalar optimization subproblems and optimizes them in a collaborative manner. This approach has been proved to be the state of the art method in solving multi-objective/many objective problems. In MOEA/D, subproblems and solutions are modelled as two sets of agents for matching. Thus, this kind of selection of promising solutions for subproblems can be regarded as a matching between subproblems and solutions. This problem could be viewed as a Stable matching problem as for school admission, hospital residents problems. Also, it can effectively resolve conflicts of interests among selfish agents in the economic market. In this talk, I will advocate the use of a simple and effective stable matching (STM) model to coordinate the selection process in MOEA/D. In this model, subproblem agents can express their preferences over the solution agents, and vice versa. The stable outcome produced by the STM model matches each subproblem with one single solution, and it tradeoffs convergence and diversity of the evolutionary search. In addition, a two-level stable matching-based selection is proposed to further guarantee the diversity of the population. More specifically, the first level of stable matching only matches a solution to one of its most preferred subproblems and the second level of stable matching is responsible for matching the solutions to the remaining subproblems. Experimental studies demonstrate that the proposed selection scheme is effective and competitive comparing to other state-of-the-art selection schemes for MOEA/D.