  Artificially evolving foraging behavior in simulated legged animals has
proved to be a notoriously difficult task. Here, we co-evolve the morphology
and controller for virtual organisms in a three-dimensional physically
realistic environment to produce goal-directed legged locomotion. We show that
following and reaching multiple food sources can evolve de novo, by evaluating
each organism on multiple food sources placed on a basic pattern that is
gradually randomized across generations. We devised a strategy of evolutionary
"staging", where the best organism from a set of evolutionary experiments using
a particular fitness function is used to seed a new set, with a fitness
function that is progressively altered to better challenge organisms as
evolution improves them. We find that an organism's efficiency at reaching the
first food source does not predict its ability at finding subsequent ones
because foraging efficiency crucially depends on the position of the last food
source reached, an effect illustrated by "foraging maps" that capture the
organism's controller state, body position, and orientation. Our best evolved
foragers are able to reach multiple food sources over 90% of the time on
average, a behavior that is key to any biologically realistic simulation where
a self-sustaining population has to survive by collecting food sources in
three-dimensional, physical environments.
