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Using Genetic Programming to Obtain a Closed-Form Approximation to a Recursive Function Evan Kirshenbaum and Henri J. Vermont HP Labs, Palo Alto, CA Evan. Kirshenbaum, AAP. Vermont hp.com Abstract.

A computer program that performs a Monte-Carlo simulation is then used to compute an approximate function for the detector based on a simulation run. The approximate function is then used for calibration in a real-world system where the accuracy (or inaccuracy) of the computed approximate function is compared to that expected by the sensor. We demonstrated the ability to obtain an approximate function with a few false positives. We developed a software framework for performing Monte-Carlo simulations of any function of interest as well as for automatically determining approximate functions for any function that is well approximated and can be approximated. In addition, the approach has the potential to perform high level optimization tasks such as identifying the minimization of overfitting while minimizing computational cost. We propose an extension of the closed form approach in which a set of samples is first sampled, a sample probability distribution is determined, and an approximate function is computed from the sample distribution as a function of inputs, with an associated model. This approach would allow arbitrary functions based on arbitrary samples or distributions to be approximated by an approximate function that can be used for calibration or decision-making. For example, an approximate P-value can be used to determine the quality of a testing process as well as an approximate P value can make diagnostic decisions based upon an actual patient. Formalization of Computational Models for Complex Variables and Networks with Deep Structural Graphs Michael G. Miller, Robert Z. Jacobi, and Brian M. Schultz University of Illinois, Urbana-Champaign, IL Thesis © 2014. Michael G. Miller () Michael Miller is a lecturer, lecturer assistant and PhD candidate in computer science at the University of Illinois, Urbana-Champaign (UIC), where he works with the team that develops CUPS. Mike currently has a PhD in computer science from Northwestern University, where he is interested in computational complexity, complexity theory, and the foundations of computer science. His current project (in progress) is to introduce a new formalism that is able to analyze complicated networks or large-scale systems that exhibit behavior similar to those of biological networks. He has a long history of research work in computational complexity (e.g., studying a distributed representation of cellular automata [CAM]).

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