Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems

TL;DR

This paper proposes methods to generate diverse, high-quality solution sets for submodular optimization problems, combining greedy sampling and evolutionary approaches, validated through experiments on benchmark functions.

Contribution

It introduces novel diversifying greedy sampling and evolutionary methods for submodular optimization, enhancing solution diversity without sacrificing quality.

Findings

Combined approaches achieve high diversity and solution quality.

Experimental results on benchmark functions validate effectiveness.

Methods outperform traditional single-solution approaches.

Abstract

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.