Abstract
We demonstrate a fully automated method for obtaining a closedform approximation of a recursive function. This method resulted from a realworld problem in which we had a detector that monitors a time series and where we needed an indication of the total number of false positives expected over a fixed amount of time. The problem, because of the constraints on the available measurements on the detector, was formulated as a recursion, and conventional methods for solving the recursion failed to yield a closed form or a closed-form approximation. We demonstrate the use of genetic programming to rapidly obtain a high-accuracy approximation with minimal assumptions about the expected solution and without a need to specify problem-specific parameterizations. We analyze both the solution and the evolutionary process. This novel application shows a promising way of using genetic programming to solve recurrences in practical settings.
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Kirshenbaum, E., Suermondt, H.J. (2004). Using Genetic Programming to Obtain a Closed-Form Approximation to a Recursive Function. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_64
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DOI: https://doi.org/10.1007/978-3-540-24855-2_64
Publisher Name: Springer, Berlin, Heidelberg
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