A hybrid genetic algorithm for group formation at workplace

Corporate university (CO) become a trend in organizations. However, functional characteristics are little explored by CO for boosting knowledge creation. One of them is functional diversity that has been pointed as a factor to increase innovation, contributing for the development of clear strategies and quick responses to changes at workplace. On the other hand, an appropriate distribution of roles helps to promote individual responsibility and group cohesion. It also contributes to the strengthening of positive interdependence of group members. These aspects are fundamental to the development of cooperative work. In this work, we propose a model for group formation for learning approaching dichotomous functional roles and preferred roles in order to improve the group performance into the courses offered by CO at Court of Justice of Goiás (CJG). From the proposed model, we constructed three group formation algorithms. The first algorithm forms groups randomly (AA). The second is a canonical genetic algorithm (CGA). The third is a hybrid genetic algorithm (HGA). After, we performed a comparative analysis of the results reached by the three algorithms. We observed that the HGA achieves superior results than the CGA and AA.