Black Box Optimization Using QUBO and the Cross Entropy Method

Black box optimization (BBO) can be used to optimize functions whose analytic form is unknown. A common approach to realize BBO is to learn a surrogate model which approximates the target black box function which can then be solved via white box optimization methods. In this paper we present our approach BOX-QUBO, where the surrogate model is a QUBO matrix. However, unlike in previous state-of-the-art approaches, this matrix is not trained entirely by regression, but mostly by classification between 'good' and 'bad' solutions. This better accounts for the low capacity of the QUBO matrix, resulting in significantly better solutions overall. We tested our approach against the state-of-the-art on four domains and in all of them BOX-QUBO showed significantly better results. A second contribution of this paper is the idea to also solve white box problems, i.e. problems which could be directly formulated as QUBO, by means of black box optimization in order to reduce the size of the QUBOs to their information-theoretic minimum. The experiments show that this significantly improves the results for MAX-$k$-SAT.