Elitist-multi-objective differential evolution for multiple question paper generation

Abstract

Student evaluation is an essential part of education and is done through the system of examinations. Examinations generally use question papers as an important component to determine the quality of the students. Examination question paper generation is a multi-constraint concurrent optimization problem. Question papers generated with random and backtracking algorithms are inefficient in handling multiple constraints such as total time for completion of the paper, total number of questions, module weightages, question types, knowledge points, difficulty level of questions etc,. In this paper we have proposed an innovative evolutionary approach that handles multi-constraints while generating question papers from a very large question bank. The proposed Elitist Multi-objective Differential Evolution Approach (EMODEA) has its advantage of simple structure, ease of use, better computational speed and good robustness. It is identified to be more suitable for combinatorial problems as compared to the generally used genetic algorithm. Experimental results indicate that the proposed approach is efficient and effective in generating near-optimal or optimal question papers that satisfy the specified requirements.