Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computing large sparse multivariate optimization problems with an application in biophysics
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Proceedings of the 2010 TeraGrid Conference
UltraScan gateway enhancements: in collaboration with TeraGrid advanced user support
Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery
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Frequently in the physical sciences experimental data are analyzed to determine model parameters using techniques known as parameter estimation. Eliminating the effects of noise from experimental data often involves Tikhonov or Maximum-Entropy regularization. These methods introduce a bias which smoothes the solution. In the problems considered here, the exact answer is sharp, containing a sparse set of parameters. Therefore, it is desirable to find the simplest set of model parameters for the data with an equivalent goodness-of-fit. This paper explains how to bias the solution towards a parsimonious model with a careful application of Genetic Algorithms. A method of representation, initialization and mutation is introduced to efficiently find this model. The results are compared with results from two other methods on simulated data with known content. Our method is shown to be the only one to achieve the desired results. Analysis of Analytical Ultracentrifugation sedimentation velocity experimental data is the primary example application.