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

Emre H Brookes, Borries Demeler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

54 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of GECCO 2007: Genetic and Evolutionary Computation Conference
Pages361-368
Number of pages8
DOIs
StatePublished - 2007
Event9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London, United Kingdom
Duration: Jul 7 2007Jul 11 2007

Other

Other9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
CountryUnited Kingdom
CityLondon
Period7/7/077/11/07

Fingerprint

Regularization
Genetic algorithms
Genetic Algorithm
Experimental Data
Experiment
Experiments
Sedimentation
Maximum Entropy
Goodness of fit
Initialization
Model
Parameter estimation
Parameter Estimation
Mutation
Entropy
Ultracentrifugation

Keywords

  • Analytical ultracentrifugation
  • Genetic algorithm
  • Inverse problem
  • Regularization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Cite this

Brookes, E. H., & Demeler, B. (2007). Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments. In Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference (pp. 361-368) https://doi.org/10.1145/1276958.1277035

Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments. / Brookes, Emre H; Demeler, Borries.

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference. 2007. p. 361-368.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Brookes, EH & Demeler, B 2007, Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments. in Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference. pp. 361-368, 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007, London, United Kingdom, 7/7/07. https://doi.org/10.1145/1276958.1277035
Brookes EH, Demeler B. Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments. In Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference. 2007. p. 361-368 https://doi.org/10.1145/1276958.1277035
Brookes, Emre H ; Demeler, Borries. / Parsimonious regularization using genetic algorithms applied to the analysis of analytical ultracentrifugation experiments. Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference. 2007. pp. 361-368
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