Genetic algorithms optimization approach supported by the first-order derivative and Newton–Raphson methods: Application to fluorescence spectroscopy

Fisz, J.J.; Buczkowski, M.; Budziński, Maciej P.; Kolenderski, Piotr

doi:10.1016/j.cplett.2005.03.044

Cited by 7 publications

(13 citation statements)

References 2 publications

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“…The genetic algorithm Genetic algorithms (GA) are computational methods, which are inspired by the principal ideas of biological evolution (18), and used to construct numerical optimization techniques suitable for the analyses of an ill-behaved search space. Recently GA was implemented in fluorescence spectroscopy (19); here this approach is employed for the first time, to our knowledge, for analysis of fluorescent depolarization data collected by the TCSPC technique.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Fluorescence Depolarization Studies of Filamentous Actin Analyzed with a Genetic Algorithm

Marushchak

Grenklo

Johansson

et al. 2007

Biophysical Journal

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A new method, in which a genetic algorithm was combined with Brownian dynamics and Monte Carlo simulations, was developed to analyze fluorescence depolarization data collected by the time-correlated single photon-counting technique. It was applied to studies of BODIPY-labeled filamentous actin (F-actin). The technique registered the local order and reorienting motions of the fluorophores, which were covalently coupled to cysteine 374 (C374) in actin and interacted by electronic energy migration within the actin polymers. Analyses of F-actin samples composed of different fractions of labeled actin molecules revealed the known helical organization of F-actin, demonstrating the usefulness of this technique for structure determination of complex protein polymers. The distance from the filament axis to the fluorophore was found to be considerably less than expected from the proposed position of C374 at a high filament radius. In addition, polymerization experiments with BODIPY-actin suggest a 25-fold more efficient signal for filament formation than pyrene-actin.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Fluorescence Depolarization Studies of Filamentous Actin Analyzed with a Genetic Algorithm

Marushchak

Grenklo

Johansson

et al. 2007

Biophysical Journal

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“…In the parameters set, we distinguish two subsets which include linear and nonlinear parameters, respectively. The latter are subjects to the GA optimization, while the former are found, for each individual, by the Newton-Raphson minimization of a quadratic form set up by the v 2 merit function [15]. In our case, the linear parameters are the decays amplitudes A c .…”

Section: Illustrative Examplementioning

confidence: 99%

“…The increasing number of dimensions of the searched parameters space depreciates usability of the gradient expansion method, successfully applied in the symmetric rotor case [13]. The measure presented in this work in order to surmount these difficulties consists in applying the genetic algorithm (GA) method [14], as in [13,15], to localize presumable global minimum of the v 2 merit function, whereafter using the local gradient search algorithm to refine the values of the recovered model parameters. This approach merges good exploration ability of the GA and a convergence speed-up of the gradients method in the vicinity of the minimum.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric rotor-like probes to polarized fluorescence study of the macroscopically oriented uniaxial media: Model parameters recognition

Buczkowski

Fisz

2008

Chemical Physics

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“…In this section we come back to the GA-NR algorithm introduced in our recent publication 13 and mentioned in short in the Introduction. We here want to add a few important explanatory comments on the application of the GA-NR optimizer to linear and weakly nonlinear model parameters that occur in the same optimization problem together with the nonlinear parameters.…”

Section: Optimization Of Linear Weakly Nonlinear and Nonlinear Model ...mentioning

confidence: 99%

“…In ref 13 we have discussed the methods for reduction of the number of fitted nonlinear and linear model parameters appearing in the nonlinear model functions. Such methods may eliminate essential problems with the "inconvenient" nonlinear and linear fitted parameters, for which the prediction of initial guesses in the GE optimization or their most adequate upper and lower bounds in the GA optimization represents a serious difficulty.…”

Section: Introductionmentioning

confidence: 99%

Combined Genetic Algorithm and Multiple Linear Regression (GA-MLR) Optimizer: Application to Multi-exponential Fluorescence Decay Surface

Fisz

2006

J. Phys. Chem. A

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The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi-linear combinations of nonlinear functions, is indicated. The VP algorithm does not distinguish the weakly nonlinear parameters from the nonlinear ones and it does not apply to the model functions which are multi-linear combinations of nonlinear functions.

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Genetic algorithms optimization approach supported by the first-order derivative and Newton–Raphson methods: Application to fluorescence spectroscopy

Cited by 7 publications

References 2 publications

Fluorescence Depolarization Studies of Filamentous Actin Analyzed with a Genetic Algorithm

Fluorescence Depolarization Studies of Filamentous Actin Analyzed with a Genetic Algorithm

Asymmetric rotor-like probes to polarized fluorescence study of the macroscopically oriented uniaxial media: Model parameters recognition

Combined Genetic Algorithm and Multiple Linear Regression (GA-MLR) Optimizer: Application to Multi-exponential Fluorescence Decay Surface

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