Heuristic Bayesian Method for Global Optimization
Vaida Bartkutė-Norkūnienė · 2012
This article is dealing with investigation of heuristic Bayesian method (HBM) for global opti- mization. The Bayesian search algorithm with memory restricted by one objective function measurement is introduced. HMB is constructed adding the Metropolis Acceptance Criterion (MAC) and changing var- iance parameter to classical Bayesian optimization algorithm. The global convergence property of the al- gorithm proposed is investigated by computer simulation for several test functions.