Algorithm Development for the Bayesian Optimization of Complex Data Sets

Abstract

Optimization is a subfield of applied mathematics and is used in the fields of nano-medicine, applied sciences and computational biology extensively. The recent trends of optimizations are focused on the algorithm development of nonlinear programs for complex data sets. During this research, an algorithm is developed for the application of “Bayesian Optimization”, for the selection of the most accurate hyper-parameter, using the Python framework as well as the MatlabTM 2020 packages. The developed model is then implemented on the experimental data sets, extracted from the online repositories. To ensure the optimalilty of the forecasting, the algorithms are exploited in detailed manner during this research.