The paper linked above does directly address the case of multiple experiments occurring in the same context. They address this with hill-climbing over those 180 different variations. The use of a bayesian linear regression takes place of the exploration found with Thompson sampling.
You're right, the paper linked above is a different way of solving the same problem. In their case they use a model to decide which website variants to show. Their model accounts for independent effects and pairwise dependencies. Evolution allows you to optimize without needing an explicit model.
I don't think they account for potentially changing conversion rates over time or delayed conversions.
Aside from that, I'd be curious to see how these two approaches compare in a real-life situation.
