Evolving behaviors for a swarm of unmanned air vehicles

We have previously reported on a project involving the control of a swarm of unmanned air vehicles (UAVs) carrying out search or search-and-destroy missions. We developed and tested (in simulation) a number of strategies for swarm control, and proposed systematic evaluation techniques and performance metrics. In this paper we report some additional results in which we evolved some of the swarm control parameters using a genetic algorithm (GA). While the improvements were modest, the results show how evolutionary computing algorithms can be used to facilitate the design of swarm control algorithms.