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Artificially evolving foraging behavior in simulated legged animals hasproved to be a notoriously difficult task. Here, we co-evolve the morphologyand controller for virtual organisms in a three-dimensional physicallyrealistic environment to produce goal-directed legged locomotion. We show thatfollowing and reaching multiple food sources can evolve de novo, by evaluatingeach organism on multiple food sources placed on a basic pattern that isgradually randomized across generations. We devised a strategy of evolutionary"staging", where the best organism from a set of evolutionary experiments usinga particular fitness function is used to seed a new set, with a fitnessfunction that is progressively altered to better challenge organisms asevolution improves them. We find that an organism's efficiency at reaching thefirst food source does not predict its ability at finding subsequent onesbecause foraging efficiency crucially depends on the position of the last foodsource reached, an effect illustrated by "foraging maps" that capture theorganism's controller state, body position, and orientation. Our best evolvedforagers are able to reach multiple food sources over 90% of the time onaverage, a behavior that is key to any biologically realistic simulation wherea self-sustaining population has to survive by collecting food sources inthree-dimensional, physical environments.

Evolution of sustained foraging in 3D environments with physics