Abstract
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