Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems
This addresses the need for diverse solutions in real-world optimisation problems, but it is incremental as it builds on existing submodular optimisation methods.
The paper tackles the problem of generating diverse sets of high-quality solutions for monotone submodular optimisation problems, introducing greedy sampling and evolutionary diversity optimisation approaches that achieve large diversity in experimental benchmarks.
Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular optimisation problems. We first present diversifying greedy sampling approaches and analyse them with respect to the diversity measured by entropy and the approximation quality of the obtained solutions. Afterwards, we introduce an evolutionary diversity optimisation approach to further improve diversity of the set of solutions. We carry out experimental investigations on popular submodular benchmark functions that show that the combined approaches achieve high quality solutions of large diversity.