A data warehouse is designed for answering analytical queries. These queries are usually long, complex and ad hoc in nature and their response time is high when processed against an exponentially growing data warehouse. Materialising views has been found to be an effective tool for reducing this response time. All views cannot be materialised on account of resource constraints. Further, optimal view selection is shown to be an NP-complete problem. This necessitates selection of an appropriate set of views, from amongst all possible views that reduce the query response time. Most of the view selection algorithms are greedy or evolutionary. In this paper, a differential evolution view selection algorithm (DEVSA) that selects the Top-K views from a multi-dimensional lattice is proposed. Further, it is shown that DEVSA when compared to the greedy, genetic and memetic-based view selection algorithms selects comparatively better quality views for higher dimensional datasets.