A data-driven hybrid identification method based on genetic and gradient-based algorithms is described in details.
We also show that our approach achieves statistical equivalent results when compared to a state-of-the-art method for replica identification based on genetic programming, without the burden and cost of any training process.
Commonly used methods of HPAI variant identification are based on genetic analysis, mainly polymerase chain reaction (PCR) technique.
In this work, we propose a new identification hybrid strategy based on genetic algorithm with parallel selection (GAPS) and artificial neural network (ANN).
A model-based control design approach, incorporating identification of the system model based on genetic algorithm is carried out to design the controller.
There will be better identification of patient populations based on genetic factors and biomarkers.
The major bottle-neck of the phenotype-driven approach used to be the identification of the causative mutation based on genetic mapping followed by sequencing of candidate genes.
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.
The identification is carried out 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 moving vehicles.
Our regression model allowed the identification of patients at high risk for metastasis even in an organ-confined situation based on genetic alterations of the primary tumour.
