Contemplating Crossover Operators of Genetic Algorithm for Student Group Formation Problem

In many industries, major projects are performed in groups. Keeping this in mind, many academic courses are fitted with syllabus that boosts the students’ group learning and group working skills. Unfortunately, optimal group formation among students is a challenging task. Genetic algorithm can be applied to solve this problem which is evident from various literatures. Genetic algorithms are purely search techniques that imitate the process of natural selection. Genetic algorithm comprises of 3 important steps viz. selection, crossover and mutation. To quickly get an optimal value, it is highly essential that a proper combination of effective selection, crossover and mutation technique be used. This paper presents a comparison of three crossover techniques viz. Partially Mixed Crossover, Order Crossover and Edge Recombination Crossover with respect to quickly getting an optimal value, keeping the other two techniques (Selection and Mutation) static. A C program was coded to check the effectiveness of these crossover techniques with respect to optimal search of a feasible solution.