We're using a genetic algorithm to train neural networks to deduce what type of neural networks can generalize better. It is current theory that the simplest model that fits a problem is the best model, but we are predicting that a bit more complexity than the simplest model will increase its generalization. Our environment is a simple map traversal that mimics race tracks and mazes; The individuals are given input corresponding to speed, direction, and distance to walls; the output includes doing nothing, speeding up, slowing down, turning left, and turning right. We will compare the results of a neural network training vs testing on a map.