Multi-objective Genetic Algorithm in Solving Conflicted Goals for Questions Generating Problem

Multi-objective genetic algorithm (MOGA) has been used for more than a decade to solve real-world optimization problems that have several, and often conflicting objectives. In this research, the conflicting objectives of achieving the maximum accuracy of the solution and at the same time minimizing the redundancy of the optimal solutions in retrieving the best set of exam questions for academicians for a particular subject are highlighted. Hence, the aim of this paper is to solve the multi-objective problem in a chromosome (solution) and also to maintain the fitness of the chromosome. The results of this research are measured based on the similarity achieved between the obtained and desired solutions. By using MOGA, a promising result is obtained with the maximum accuracy and simultaneously, minimizing the redundancy of the genes in a solution.