An efficient evolutionary algorithm for subset selection with general cost constraints

N/ACitations
Citations of this article
32Readers
Mendeley users who have this article in their library.

Abstract

In this paper, we study the problem of selecting a subset from a ground set to maximize a monotone objective function f such that a monotone cost function c is bounded by an upper limit. State-of-the-art algorithms include the generalized greedy algorithm and POMC. The former is an efficient fixed time algorithm, but the performance is limited by the greedy nature. The latter is an anytime algorithm that can find better subsets using more time, but without any polynomial-time approximation guarantee. In this paper, we propose a new anytime algorithm EAMC, which employs a simple evolutionary algorithm to optimize a surrogate objective integrating f and c. We prove that EAMC achieves the best known approximation guarantee in polynomial expected running time. Experimental results on the applications of maximum coverage, influence maximization and sensor placement show the excellent performance of EAMC.

Cite

CITATION STYLE

APA

Bian, C., Feng, C., Qian, C., & Yu, Y. (2020). An efficient evolutionary algorithm for subset selection with general cost constraints. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence (pp. 3267–3274). AAAI press. https://doi.org/10.1609/aaai.v34i04.5726

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free