Characterizing signal behaviour using genetic programming
Our overall goal is to detect automatically that a signal begins to deviate from its previous behaviours, using no other information than a sequence of samples of the signal. In order to detect such changes we use genetic programming to evolve an expression describing how the signal varies over time. One major difficulty when observing such signals is that they typically contain noise and other disturbances. Such disturbances makes it more difficult to find a useful expression characterizing the signal. We have derived a new method that simultaneously evolves a numeral denoting the number of neighbours to use in a moving average of the signal, and an expression characterizing the smoothed signal.
Unable to display preview. Download preview PDF.
- 1.P. J. Angeline. Evolutionary algorithms and emergent intelligence. PhD thesis, Ohio State University, 1993.Google Scholar
- 2.S. Džeroski and I. Petrovski. Discovering dynamics with genetic programming. Technical report, Unstitute Jožef Stefan, 1994.Google Scholar
- 3.Erlbaum, editor. Reducing bias and inefficiency in the selection algorithm. International Conference on Genetic Algorithms and their Applications, 1985.Google Scholar
- 4.J. R. Koza. Genetic Programming — On the programming of computers by means of natural selection. MIT Press, Cambridge, Mass., 1992.Google Scholar
- 5.J. Y. B. Lee and P. C. Wong. The effect of function noise on GP efficiency. In X. Yao, editor, Progress in Evolutionary Computation, volume 956 of Lecture Notes in Artificial Intelligence, pages 1–16. Springer-Verlag, Heidelberg, Germany, 1995.Google Scholar
- 6.A. V. Oppenheim and A. S. Willsky. Signals and Systems. Prentice-Hall, 1983.Google Scholar
- 7.K. C. Sharman, A. I. E. Alcazar, and Y. Li. Evolving signal processing algorithms by genetic programming. Technical report, University of Glasgow, 1994.Google Scholar
- 8.W. A. Tackett. Mining the genetic program. IEEE Expert, jun 1995.Google Scholar