Evolutionary algorithms (EA) have become a standard tool for the optimization of complex composite structures because of their ability to solve combinatorial problems. However, several studies have shown that simpler algorithms, such as stochastic hill climbers (SHC) can be more e cient even on problems designed to demonstrate EAs superiority, such as the Royal Road problem. The present paper compares the performance of a variant of EA, the univariate marginal distribution algorithm (UMDA) with that of an SHC on di erent tness landscapes found in laminate optimization problems and identi es factors that in uence the algorithms' relative performance. In particular, it is found that mUMDA, a hybrid algorithm that combines UMDA's global distribution learning and SHC's local random search, outperforms SHC on large, highly constrained problems and on multimodal problems.