.png){kind=icon}
返回
Constrained Monotone k-Submodular Function Maximization Using Multiobjective Evolutionary Algorithms With Theoretical Guarantee
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2017.2749263.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The problem of maximizing monotone k-submodular functions under a size constraint arises in many applications, and it is NP-hard. In this paper, we propose a new approach which employs a multiobjective evolutionary algorithm to maximize the given objective and minimize the size simultaneously. For general cases, we prove that the proposed method can obtain the asymptotically tight approximation guarantee, which was also achieved by the greedy algorithm. Moreover, we further give instances where the proposed approach performs better than the greedy algorithm on applications of influence maximization, information coverage maximization, and sensor placement. Experimental results on real-world data sets exhibit the superior performance of the proposed approach.
Keyword:
Constrained optimization
experimental studies
multiobjective evolutionary algorithms (MOEAs)
submodular optimization
theoretical analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
引用论文
分享收藏Development of gene-based markers from functional Arabidopsis thaliana genes involved in phosphorus homeostasis and mapping in Brassica napus.png)
Euphytica
EuphyticaIF0
PREAI
分享收藏分享收藏Functional morphology underlies performance differences among invasive and non-invasive ruderal Rubus speciesOecologia
OecologiaIF0
PREAI
分享收藏
Vocal Development and Its Differentiation in a Non-Songbird: the Collared Dove (Streptopelia Decaocto)Behaviour
BehaviourIF0
PREAI
分享收藏
查看更多内容