  We investigate the matching of agents to resources in a computational ecology
configured to present heterogeneous resource patches to evolving, neurally
controlled agents. We repeatedly find a nearly optimal, ideal free distribution
(IFD) of agents to resources. Deviations from IFD are shown to be consistent
with models of human foraging behaviors, and possibly driven by spatial
constraints and maximum foraging rates. The lack of any model parameters
addressing agent foraging or clustering behaviors and the biological
verisimilitude of our agent control systems differentiates these results from
simpler models and suggests the possibility of exploring the underlying
mechanisms by which optimal foraging emerges.
