论文标题
计算单调次试验优化问题的各种解决方案集
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems
论文作者
论文摘要
子管函数允许对许多现实世界优化问题进行建模。本文介绍了用于计算多种高质量解决方案的方法，以解决supply质优化问题。我们首先介绍多样化的贪婪采样方法，并就通过熵衡量的多样性以及所获得的溶液的近似质量进行分析。之后，我们引入了一种进化多样性优化的方法，以进一步改善解决方案集的多样性。我们对流行的基本基准功能进行了实验研究，这些功能表明，合并的方法可以实现高质量的多样性解决方案。
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.
