The project presents case studies with various test functions to illustrate the capabilities and limitations of Bayesian Optimization, particularly in higher-dimensional problems. Below is a figure ...

This repository provides a framework for optimizing GaN HEMTs using Bayesian algorithms and GPR models. It uses Latin Hypercube Sampling (LHS) for initial data generation, trains a GPR model for ...

Advances in evolutionary computation have demonstrated that Evolutionary Algorithms (EAs) proposed in this area are a solid alternative for solving combinatorial and continuous optimization problems.

Bayesian network structure learning is one of the current research hotspots in fields such as statistics and machine learning. Although it has great potential and application prospects, when there are ...

Significant strides have been made in supervised learning settings thanks to the successful application of deep learning. Now, recent work has brought the techniques of deep learning to bear on ...

Bayesian-optimization algorithms have proved exceptionally effective in other applications, but the authors are among the first to develop a reaction-optimization toolkit that uses this approach.