In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method combines an adaptive search procedure with a fuzzy logic pricing step to set reserve prices and is suitable for non-stationary environments. In the fuzzy logic pricing step, we take the gap between the winning bid and second highest bid into account and show that this leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.