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Genetic Algorithm Optimization for Obtaining Accurate Molecular Weight Distributions from Sedimentation Velocity Experiments

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Analytical Ultracentrifugation VIII

Part of the book series: Progress in Colloid and Polymer Science ((PROGCOLLOID,volume 131))


Sedimentation experiments can provide a large amount of information about the composition of a sample, and the properties of each component contained in the sample. To extract the details of the composition and the component properties, experimental data can be described by a mathematical model, which can then be fitted to the data. If the model is nonlinear in the parameters, the parameter adjustments are typically performed by a nonlinear least squares optimization algorithm. For models with many parameters, the error surface of this optimization often becomes very complex, the parameter solution tends to become trapped in a local minimum and the method may fail to converge. We introduce here a stochastic optimization approach for sedimentation velocity experiments utilizing genetic algorithms which is immune to such convergence traps and allows high-resolution fitting of nonlinear multi-component sedimentation models to yield distributions for sedimentation and diffusion coefficients, molecular weights, and partial concentrations.

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genetic algorithm


residual mean square deviation


non-negatively constrained linear least squares


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Correspondence to Emre Brookes .

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Christine Wandrey Helmut Cölfen

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Brookes, E., Demeler, B. Genetic Algorithm Optimization for Obtaining Accurate Molecular Weight Distributions from Sedimentation Velocity Experiments. In: Wandrey, C., Cölfen, H. (eds) Analytical Ultracentrifugation VIII. Progress in Colloid and Polymer Science, vol 131. Springer, Berlin, Heidelberg.

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