The recently introduced real-valued gene-pool optimal mixing evolutionary algorthm (RV-GOMEA) has been shown to be among the state of the art for solving gray-box optimization problems where partial evaluations can be leveraged. A core strength is its ability to effectively exploit the linkage structure of a problem, which often is unknown a priori and has to be learned online. Previously published work on RV-GOMEA, however, demonstrated excellent scalability when the linkage structure is prespecified appropriately. A mutual information-based metric to learn linkage structure online, as commonly adopted in EDA’s and the original discrete version of the gene-pool optimal mixing evolutionary algorithm, did not lead to similarly excellent results, especially in a black-box setting. In this article, the strengths of RV-GOMEA are combined with a new fitness-based linkage learning approach that is inspired by differential grouping that reduces its computational overhead by an order of magnitude for problems with fewer interactions. The resulting new version of RV-GOMEA achieves scalability similar to when a predefined linkage model is used, outperforming also, for the first time, the EDA AMaLGaM upon which it is partially based in a black-box setting where partial evaluations cannot be leveraged. 1 1 This article is extended from the MSc thesis of Chantal Olieman, available at https://repository.tudelft.nl/ [24] .