  Working with exhaustive search on large dataset is infeasible for several
reasons. Recently, developed techniques that made pattern set mining feasible
by a general solver with long execution time that supports heuristic search and
are limited to small datasets only. In this paper, we investigate an approach
which aims to find diverse set of patterns using genetic algorithm to mine
diverse frequent patterns. We propose a fast heuristic search algorithm that
outperforms state-of-the-art methods on a standard set of benchmarks and
capable to produce satisfactory results within a short period of time. Our
proposed algorithm uses a relative encoding scheme for the patterns and an
effective twin removal technique to ensure diversity throughout the search.
