Hyper-heuristics are high-level search techniques which improve the performance of heuristics operating at a higher heuristic level. Usually, these techniques automatically generate or select new simpler components based on the feedback received during the search. Estimation of Distribution Algorithms (EDAs) have been applied as hyper-heuristics, using a probabilistic distribution model to extract and represent interactions between heuristics and its low-level components to provide high-valued problem solutions. In this paper, we consider an EDA-based hyper-heuristic framework which encompasses a Heuristic Selection approach aiming to find best combinations of different known heuristics. A surrogate assisted model evaluates the new heuristic combinations sampled by the EDA probabilistic model using an approximation function. We compare our proposed approach named Heuristic Selection based on Estimation of Distribution Algorithm (HSEDA) with three state-of-the-art algorithms for the Travelling Thief Problem (TTP). The experimental results show that the approach is competitive, outperforming the other algorithms on most of the medium-sized TTP instances considered in this paper.