We focus on basin-hopping (BH) , which is a popular choice for CSP . In the BH approach, Monte Carlo (MC) sampling is used to generate possible crystal structures at a given composition. For each generated structure, a local energy minimisation is performed to find the nearest minimum. Given the locally optimised crystal structure with its calculated energy, the code will have a set of possible actions that generate the next trial structure according to a set of deterministic or probabilistic rules. Intuitively, the choice of action will determine how "far" the new trial structure is from the current structure on the PES of that composition. For example, a "local action" may swap two atoms, and a more transformative action may replace the current structure with a new randomly created one (i.e., this action "starts the search again"). A typical policy would be a fixed discrete probability distribution of actions, which is used to randomly draw an action every time BH generates a trial structure. While such policies can effectively balance actions with a good tuning of action probabilities, which requires historic data and is not trivial, they also remain fixed during the run and are applied independently of the current structure or other contextual factors.