Edinburgh Napier University

  Recent works have shown that the idea of mining search spaces to train machine learning models can facilitate increasing understanding of variable importance in optimisation problems. However , so far, the problems studied have typically either been toy benchmarks or have not had known ground-truth importances. A newly established combinatorial optimisation benchmark domain, Polynomial Unconstrained Binary Optimisation with variable importance (PUBOi), facilitates problem instances with tunable variable importance. In this work, we explore the potential of using explainable artificial intelligence (XAI) attribution methods for uncovering variable importances from mined search space models on PUBOi instances with ground-truth importances. We compare learning algorithms, XAI methods, and sample sizes used to train the models to better understand which techniques are promising in this context. The analysis lays the groundwork for future possibilities of using XAI on mined search spaces models during search to adapt or switch operators for more effective optimisation.