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Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences
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Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences

Chao Qian
Danyang Liu
Chao Feng
K. Tang
ArXiv (abs)PDFHTML

Papers citing "Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences"

3 / 3 papers shown
Title
Runtime Analysis of Evolutionary NAS for Multiclass Classification
Runtime Analysis of Evolutionary NAS for Multiclass Classification
Zeqiong Lv
Chao Qian
Yun-Pei Liu
Jiahao Fan
Yanan Sun
40
0
0
06 Jun 2025
Fast Pareto Optimization Using Sliding Window Selection
Fast Pareto Optimization Using Sliding Window Selection
Frank Neumann
Carsten Witt
31
5
0
11 May 2023
Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer
Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer
Peng Yang
Laoming Zhang
Haifeng Liu
Guiying Li
57
35
0
05 May 2023