Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms-Reference-Cited by-同舟云学术

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Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms

Published:2019-10 Issue: Volume:275 Page:279-294
ISSN:0004-3702
Container-title:Artificial Intelligence
language:en
Short-container-title:Artificial Intelligence

Author:

Qian Chao,Yu Yang,Tang Ke,Yao Xin,Zhou Zhi-Hua

Funder

NSFC

Program for Guangdong Introducing Innovative and Enterpreneurial Teams

Shenzhen Peacock Plan

National Key R&D Program of China

Collaborative Innovation Center of Novel Software Technology and Industrialization

Publisher

Elsevier BV

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference48 articles.

1. An 0.828-approximation algorithm for the uncapacitated facility location problem;Ageev;Discrete Appl. Math.,1999

2. Theory of Randomized Search Heuristics: Foundations and Recent Developments;Auger,2011

3. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms;Bäck,1996

4. Guarantees for greedy maximization of non-submodular functions with applications;Bian,2017

5. A general approach to running time analysis of multi-objective evolutionary algorithms;Bian,2018

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