A genetic approach for tri-objective optimization in team formation

The advent of social and collaboration networks has resulted in different methods of forming large groups to deal with complex tasks. Team Formation (TF) in online social networks is crucial in several applications, such as collaborative software development and community based question and answer forums. The problem involves the formation of expert teams from a social network who can collaborate effectively under multiple constraints. In a practical scenario, the problem involves a minimization of the following major objectives: communication cost, expert cost and the size of the team. The minimization is performed by finding Pareto-Optimal teams, in which no team dominates the solution teams in terms of the three chosen objectives. Existing approaches use approximation algorithms and cannot be easily extended to incorporate additional objectives. Therefore, an optimization framework the Non-dominated Sorting Genetic Algorithm for Team Formation (NSGA-II TF) is proposed which is robust and extensible. A mapping scheme is defined for representing the chromosome in the NSGA-II algorithm to satisfy the constraints of the TF problem. The scalability, precision and recall for NSGA-II TFare evaluated and it is observed that it results in teams with minimized cardinality, communication and expert cost.