652 citations · 784 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…