Abstract
Previous research shows that teams with diverse background and skills can outperform homogeneous teams. However, people often prefer to work with others who are similar and familiar to them and fail to assemble teams with high diversity levels. We study the problem of team formation considering a pool of individuals who possess different skills and characteristics, and a social network that captures the familiarity among these individuals. The goal is to assign all individuals in diverse teams but based on their social connections, thereby allowing them to preserve a level of familiarity. To address this problem, we implement an algorithm based on the NSGA-II genetic optimization that splits members into well-connected and diverse teams within a social network. It optimizes measures of team communication cost and diversity in O(n^2) time. We tested the algorithm on three empirically collected team formation datasets and against three benchmark algorithms. The experimental results confirm that the proposed algorithm was successful at forming teams that have both diversity in member attributes and previous connections between members. We discuss the benefits of using computational approaches to augment team formation and composition.
