Online optimization of replacement policies using learning automata

A global optimization algorithm operating online in a stochastic multi-teacher environment is suggested. An application example introduces a new perspective for solving some optimization problems dealing with reliability. First, a hybrid scheme combining reinforcement-based learning automata and confidence probabilistics is developed for a single-teacher environment. The scheme is able to find the optimal solution with high confidence, yet providing a sequence of search actions that converge to the minimal loss. In addition, the suggested approach provides an on-line measure of the confidence to the current solution. Second, a multi-teacher environment is considered.