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GECCO
2007
Springer

Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments

9 years 1 months ago
Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments
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 ve...
Emre H. Brookes, Borries Demeler
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2007
Where GECCO
Authors Emre H. Brookes, Borries Demeler
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