Effective foraging in a predictable local environment requires coordinating movement with the observable spatial context, in a word, navigation. Distinct from search, navigating to specific areas known to be valuable entails its own particularities. How space is understood through vision and parsed for navigation is often examined experimentally, with limited ability to manipulate sensory inputs and probe into the algorithmic level of decision-making. As a generalizable, minimal alternative to empirical means, embodied neural networks were evolved and studied to explore information processing algorithms an organism may use for visual spatial navigation. Surprisingly, three distinct classes of algorithms emerged, each with its own set of rules and tradeoffs, and each appear to be highly relevant to observable biological navigation behaviors.