Mice and Cheese Challenge: Exploring NEAT (NeuroEvolution of Augmented Topologies) 🧠

This is the NEAT(Neuro Evolution of Augmented Topologies) algorithm that I programmed during the end of my 9th grade year.

The objective for the mice in the scenario is to eat as many cheese as they can while avoiding poison. The scenario consists of 100 mice with initially rudimentary neural networks(1 input layer and 1 hidden layer). The mechanism of NEAT complexifies the topology of the neural network and optimizes its weight connections simultaneously. This genetic algorithm is robust towards any N-dimensional data scenario, since it has the ability to complexify and optimize its topology to make it suitable to explore arbitrary data of arbitrary dimensions.

As the generation progresses, the ratio of the total number of cheese eaten by the total number of poison eaten increases, making the NEAT algorithm to be a successful optimization method for the mice scenario.