The German Research Foundation (DFG) granted a new research project on Theory of Estimation-of-Distribution Algorithms (TEDA). Commonly applied heuristics to efficiently solve large optimization problems, such as evolutionary computation, are inspired by nature. While evolutionary algorithms (EAs), one such framework, have been studied for more than three decades, the theoretical analysis of estimation-of-distribution algorithms (EDAs) only gained momentum recently. The goal of this project is to reduce the gap between the theoretical results of EAs and EDAs, providing a more complete picture of the capabilities of evolutionary computation in general. While EAs and EDAs are structurally different, their optimization efficiencies are surprisingly correlated. Nonetheless, both approaches have their individual advantages, which the principal investigators of the project, Tobias Friedrich and Timo Kötzing, want to uncover.