Machine learning - neuroevolution for designing chip circuits/pathfinding

Sammanfattning: Neural Networks have been applied in numeral broad categories of work. Such as classification, data processing, robotics, systemcontrol e.t.c. This thesis compares using traditional methods of the routing process in chip circuit design to using a Neural Network trained with evolution. Constructing and evaluating a chip design is a complicated thing, where a lot of variables have to be accounted for and therefore a simplified evaluation and design process is used in order to train the network and compare the results. This was done by constructing simple test cases and running the algorithms BFS, A'Star and the neural network and comparing the paths each algorithm found using a fitness function. The results were that BFS and A'Star both performed better on complex circuits, but the neural network was able to create better paths on very small and niche circuits. The conclusion of the study is that the neural network approach is not able to compete with the standard industry methods of the routing process, but we do not exclude the possibility that with a better designed Fitness function, this could be possible.