Low-level Edge Detection Using Genetic Programming: performance, specificity and application to real-world signals

Genetic Programming (GP) is a powerful machine learning technique derived from Genetic Algorithms. We use GP to produce high-performance edge detectors in 1dimensional signals. Using the theory of edge detection to assess candidate detectors produced by GP, we show that this technique can produce detectors that often outperform the established ‘optimal’ detectors. Further, we show that detectors can be evolved to be specific to the training data, offering the capability to produce tailor-made edge detectors. Such detectors are shown to perform better on the data set than other evolved detectors.