In this paper, we perform an ablation study of eatfa, a neuro-evolved foraging algorithm that has recently been shown to forage efficiently under different resource distributions. Through selective disabling of input signals, we identify a emph{sufficiently} minimal set of input features that contribute the most towards determining search trajectories which favor high resource collection rates. Our experiments reveal that, independent of how the resources are distributed in the arena, the signals involved in imparting the controller the ability to switch from searching of resources to transporting them back to the nest are the most critical. Additionally, we find that pheromones play a key role in boosting performance of the controller by providing signals for informed locomotion in search for unforaged resources.
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