Learn to Walk w/Genetic Algorithm
For this project, I created a walking simulation, that is trained using a genetic algorithm. The walkers are created using multiple parameters such as node radius, restitution, friction, joint length and many more. The genetic algorithm works by taking the best parameters from a pool (each generation) and permuting those parameters by a normal model. Eventually the walkers learn how to walk! Each generation contains a set number of walkers (the population) and the parent count is some subset that will be used to "mate" and create the following generation. The entire world is contained within Box2D (a popular physics engine) and the project was written in C++. Here is the link to the repository if you would like to contribute or take a look!