Fitness-based Linkage Learning in the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm
The recently introduced Real Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has been shown to be among the state-of-the-art for solving grey-box optimization problems where partial evaluations can be leveraged. A core strength is its ability to effectively exploit the linkage structure of a problem. For many real-world optimization problems the linkage structure is unknown a priori and has to be learned online. Previously published work on RV-GOMEA however demonstrated excellent scalability only when the linkage structure is pre-specified appropriately. A mutual-information-based metric to learn linkage structure online as commonly adopted in EDA's and the original discrete version of GOMEA 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 tness-based linkage learning approach that is inspired by differential grouping, but 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 the black box case where partial evaluations can not be leveraged.
