In this study, we enhance our previous method by implementing parameters optimization/tuning mechanism, for optimizing the PSO parameters based on genetic algorithm (GA) in order to increase the classification accuracy.
Using the mouse TNF promoter sequence as the outgroup, we performed phylogenetic analyses and obtained very strong bootstrap values for the nodes of divergence of the genera using neighbor joining methods based on genetic distances (Kimura 2-parameter model) as well as maximum parsimony (Figure 3).
This work objective was to define a modeling approach based on genetic algorithm (GA) for optimizing parameters of an artificial neural network (ANN); the latter describes rabies virus production in BHK-21 cells based on empirical data derived from uniform designs (UDs) with different numbers of experimental runs.
Currently, most established cancer types and cancer grading systems (such as Gleason score for prostate cancers [ 8] and WHO grades for tumours of the central nervous system [ 9]) are not even based on genetic markers but instead on clinical parameters and phenotypic observations.
Its operating conditions were determined from the SG-SMB optimization tool based on genetic algorithm, in which the mass-transfer parameters as well as the adsorption isotherm parameters of the feed components (two amino acids) were entered as the mathematical models expressed as a function of liquid-phase composition (i.e., modifier concentration in the liquid phase).
Furthermore, a parameters estimation approach based on genetic algorithm is also developed to facilitate the transformation between the generic model and other geometrical models, as well as to improve its flexibility and applicability.
The first approach is a parameter optimization approach based on genetic algorithm (GA).
This explanation is also supported by statistical analyses of medical data [ 34], where it is suggested that the onset of menopause is largely based on genetic factors, which would be related to parameter values in our model.
This work presents our system based on genetic algorithm (GA) to automatically estimate the input parameters of the Klatt synthesizer using an analysis-by-synthesis process.
The identification is realized through a robust multi-stage optimization scheme based on genetic algorithms, which searches for the best estimates of parameters by minimizing the errors between the measured accelerations and the reconstructed accelerations from the identified parameters.
