Using artificial evolution and selection to model insect navigation

Background: An animal's behavioral strategies are often constrained by its evolutionary history and the resources available to it. Artificial evolutio n allows one to manipulate such constraints and explore how they influence evolved strategies. Here we compare the navigational strategies of flying i nsects with those of artificially evolved "animats" endowed with various mo tor architectures. Using evolutionary algorithms, we generated artificial n eural networks that controlled a virtual animat's navigation within a 2D, s imulated world. Like a flying insect, the animat possessed motors that gene rated thrust and torque, a compass, and visual sensors. Some animats were l imited to forward motion, while others could also move sideways. Animats we re selected for the precision with which they reached a target specified by a visual landmark. Results: Animats given sideways motors could alter flight direction without changing body orientation and evolved strategies similar to those of flyin g bees or wasps performing the same task. Both animats and insects first ai med at the landmark. In the last phase, both adopted a fixed body orientati on and adjusted their position to keep the landmark at a fixed retinal loca tion. Animats unable to uncouple flight direction and body orientation evol ved subtly different strategies and performed less robustly. Conclusions: This convergence between the navigational strategies of animal s and animats suggests that the insect's strategies are primarily an adapta tion to the demands of using visual information and compass direction to re ach a position in space and that they are not significantly compromised by the insect's evolutionary history.