An Efficient Evolutionary Algorithm for Minimum Cost Submodular Cover
An Efficient Evolutionary Algorithm for Minimum Cost Submodular Cover
Victoria G. Crawford
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 1227-1233.
https://doi.org/10.24963/ijcai.2019/171
In this paper, the Minimum Cost Submodular Cover problem is studied, which is to minimize a modular cost function such that the monotone submodular benefit function is above a threshold. For this problem, an evolutionary algorithm EASC is introduced that achieves a constant, bicriteria approximation in expected polynomial time; this is the first polynomial-time evolutionary approximation algorithm for Minimum Cost Submodular Cover. To achieve this running time, ideas motivated by submodularity and monotonicity are incorporated into the evolutionary process, which likely will extend to other submodular optimization problems. In a practical application, EASC is demonstrated to outperform the greedy algorithm and converge faster than competing evolutionary algorithms for this problem.
Keywords:
Heuristic Search and Game Playing: Combinatorial Search and Optimisation
Heuristic Search and Game Playing: Heuristic Search and Machine Learning