  Vital to primary visual processing, retinal circuitry shows many similar
structures across a very broad array of species, both vertebrate and
non-vertebrate, especially functional components such as lateral inhibition.
This surprisingly conservative pattern raises a question of how evolution leads
to it, and whether there is any alternative that can also prompt helpful
preprocessing. Here we design a method using genetic algorithm that, with many
degrees of freedom, leads to architectures whose functions are similar to
biological retina, as well as effective alternatives that are different in
structures and functions. We compare this model to natural evolution and
discuss how our framework can come into goal-driven search and sustainable
enhancement of neural network models in machine learning.
