Evolution of Signal Processing Algorithms using Vector Based Genetic Programming

This paper demonstrates that FIFTH^TM, a new vector-based genetic programming (GP) language, can automatically derive very effective signal processing algorithms directly from signal data. Using symbol rate estimation as an example, we compare the performance of a standard algorithm against an evolved algorithm. The evolved algorithm uses a novel approach in developing a symbol transition feature vector and achieves an impressive 97.7% overall accuracy in the defined problem domain, far exceeding the performance of the standard algorithm. These results suggest that vector based GP approaches could be useful in developing more expressive features for a large class of signal processing and classification problems.