652 citations · 784 across the 7 of their papers we have counts for
7 papers
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning
Huan Zhang, Jinliang Ding, Liang Feng +2
Dynamic environments pose great challenges for expensive optimization problems, as the objective functions of these problems change over time and thus require remarkable computatio…
A Scalable Test Problem Generator for Sequential Transfer Optimization
Xiaoming Xue, Cuie Yang, Liang Feng +3
Sequential transfer optimization (STO), which aims to improve the optimization performance on a task of interest by exploiting the knowledge captured from several previously-solved…
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach
Haokai Hong, Min Jiang, Qiuzhen Lin +1
We define very large-scale multiobjective optimization problems as optimizing multiple objectives (VLSMOPs) with more than 100,000 decision variables. These problems hold substanti…
Differentiable Search of Accurate and Robust Architectures
Yuwei Ou, Xiangning Xie, Shangce Gao +3
Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense. Among these methods, adversarial training has…
EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation
Beichen Huang, Ran Cheng, Zhuozhao Li +2
Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence. Recently, with the surge in data-inten…
A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization
Songbai Liu, Qiuzhen Lin, Jianqiang Li +1
Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively imp…
A Survey on Evolutionary Neural Architecture Search
Yuqiao Liu, Yanan Sun, Bing Xue +3
Deep Neural Networks (DNNs) have achieved great success in many applications. The architectures of DNNs play a crucial role in their performance, which is usually manually designed…