242 citations · 388 across the 13 of their papers we have counts for
9 papers · 1 filter
A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization
Yansong Huang, Zherui Zhang, Ao Jiao +2
Evolutionary multi-objective optimization (EMO) algorithms have been demonstrated to be effective in solving multi-criteria decision-making problems. In real-world applications, an…
Evolutionary Reinforcement Learning: A Survey
Hui Bai, Ran Cheng, Yaochu Jin
Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep…
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…
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
Zhichao Lu, Ran Cheng, Yaochu Jin +2
The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the develo…
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search
Haoyu Zhang, Yaochu Jin, Ran Cheng +1
The performance of a deep neural network is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network archit…
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Cheng He, Shihua Huang, Ran Cheng +2
Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is hig…
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks
Cheng He, Shihua Huang, Ran Cheng +2
Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models.Usually, the performance of such model based evolutionary algorithms is h…
Multiobjective Test Problems with Degenerate Pareto Fronts
Liangli Zhen, Miqing Li, Ran Cheng +2
In multiobjective optimisation, a set of scalable test problems with a variety of features allow researchers to investigate and evaluate the abilities of different optimisation alg…
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
Ye Tian, Ran Cheng, Xingyi Zhang +1
Over the last three decades, a large number of evolutionary algorithms have been developed for solving multiobjective optimization problems. However, there lacks an up-to-date and…