An efficient evolutionary algorithm for subset selection with general cost constraints

37Citations
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.

References Powered by Scopus

Maximizing the spread of influence through a social network

6803Citations
N/AReaders
Get full text

An analysis of approximations for maximizing submodular set functions-I

3471Citations
N/AReaders
Get full text

A Threshold of ln n for Approximating Set Cover

2332Citations
N/AReaders
Get full text

Cited by Powered by Scopus

A First Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II)

67Citations
N/AReaders
Get full text

Better approximation guarantees for the NSGA-II by using the current crowding distance

38Citations
N/AReaders
Get full text

Pareto optimization for subset selection with dynamic cost constraints

38Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

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

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 12

75%

Researcher 2

13%

Professor / Associate Prof. 1

6%

Lecturer / Post doc 1

6%

Readers' Discipline

Tooltip

Computer Science 14

82%

Physics and Astronomy 2

12%

Business, Management and Accounting 1

6%

Save time finding and organizing research with Mendeley

Sign up for free