8 citations · 16 across the 3 of their papers we have counts for
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cs.NE2023★ 8 cited
Distributed Evolution Strategies with Multi-Level Learning for Large-Scale Black-Box Optimization
Qiqi Duan, Chang Shao, Guochen Zhou +3
In the post-Moore era, main performance gains of black-box optimizers are increasingly depending on parallelism, especially for large-scale optimization (LSO). Here we propose to p…
cs.NE2023★ 5 cited
Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
Qiqi Duan, Chang Shao, Guochen Zhou +3
Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC)…
cs.NE2022★ 3 cited
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
Qiqi Duan, Guochen Zhou, Chang Shao +7
In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods (e.g., evolutionary algorithms, swarm intel…