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.

1. Introduction

Although Genetic Programming (GP) research has been ongoing for several years, there are relatively few applications in digital signal processing. Perhaps one of the reasons for this is the difficulty of applying classical GP approaches to problems that are better expressed as vector transformations. When dealing with real-world time series data, e.g. [1], researchers often pre-process the data to produce features, then use the individual feature points as the input terminal values for GP.