Autonomous Controller Design for Unmanned Aerial Vehicles using Multi-objective Genetic Programming
Choong K. Oh and Gregory J. Barlow

Autonomous navigation controllers were developed for fixed wing unmanned
aerial vehicle (UAV) applications using multi-objective genetic
programming (GP). We designed four fitness functions derived from flight
simulations and used multi-objective GP to evolve controllers able to
locate a radar source, navigate the UAV to the source efficiently using
on-board sensor measurements, and circle closely around the emitter.  
Controllers were evolved for three different kinds of radars: stationary,
continuously emitting radars, stationary, intermittently emitting radars,
and mobile, continuously emitting radars. We selected realistic flight
parameters and sensor inputs to aid in the transference of evolved
controllers to physical UAVs.