Fitness function changes to improve performance in a GA used for multi-UAV tasking

Various methods have been utilized for the cooperative tasking of unmanned aerial vehicles (UAVs), with the genetic algorithm (GA) being a technique that has proven to be versatile and effective for this use. The design and implementation of a GA is both an art and a science that brings together creativity, theoretical foundations and engineering. The focus of this paper is to show how the fitness function for a GA has been improved to meet variable mission constraints and also improve performance of the system designed to provide support for a ground station to fly cooperative missions with teams of small UAVs.