Genetic learning of virtual team member preferences

• Developed a genetic algorithm based system for learning virtual team member preferences.
• Created a simulation based system to illustrate virtual team scheduling system.
• Applied genetic algorithm based system for virtual team scheduling and showed that it helps minimize communication cost.

Virtual team members do not have complete understanding of other team members’ preferences, which makes team coordination somewhat difficult and time consuming. Traditional approaches for team coordination require a lot of inter-agent electronic communication and often result in wasted effort. Methods that reduce inter-agent communication and conflicts are likely to increase productivity of virtual teams. In this research, we propose an evolutionary genetic algorithm (GA) based intelligent agent that learns a team member preferences from past actions, and develops a team-coordination schedule by minimizing schedule conflicts between different members serving on a virtual team. Using a discrete event simulation methodology, we test the proposed intelligent agent on different virtual teams of sizes two, four, six and eight members. The results of our experiments indicate that the GA-based intelligent agent learns individual team member preferences and generates a team-coordination schedule at a lower inter-agent communication cost.