Application of the UCT Algorithm for Noisy Optimization Problems

Abstract

This paper introduces the upper confidence bound (UCB) applied to the tree (UCT) algorithm for noisy optimization problems. Although there are many studies and effective algorithms regarding optimization problems like PSO and DE, the UCB value enables us to balance exploration and exploitation in the search process and to detect the most probable area containing the optimum solution. Thus, UCB applied to tree can search the area more precisely by dividing the area, and is well known for the bandit problem and computation igo. Under the noisy condition, optimization problems are not so much deterministic problems as probabilistic ones like the bandit problem. Therefore, we propose a novel approach to the situation based on the bandit algorithm as typified by UCT. We performed comparative experiments in continuous optimization problems with additive noise to confirm its convergence speed and ability for best area identification.