587 citations · 2k across the 58 of their papers we have counts for
60 papers
EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms
Mai Peng, Delaram Yazdani, Zeneng She +9
Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algo…
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search
Shangshang Yang, Haiping Ma, Cheng Zhen +4
Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students' proficiency in knowledge concepts for subsequent adaptive tasks. However, due t…
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
Guoyang Xie, Jinbao Wang, Yawen Huang +4
The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural feature…
Lightweight Structure-aware Transformer Network for VHR Remote Sensing Image Change Detection
Tao Lei, Yetong Xu, Hailong Ning +4
Popular Transformer networks have been successfully applied to remote sensing (RS) image change detection (CD) identifications and achieve better results than most convolutional ne…
Identification of Energy Management Configuration Concepts from a Set of Pareto-optimal Solutions
Felix Lanfermann, Qiqi Liu, Yaochu Jin +1
Implementing resource efficient energy management systems in facilities and buildings becomes increasingly important in the transformation to a sustainable society. However, select…
What makes a good data augmentation for few-shot unsupervised image anomaly detection?
Lingrui Zhang, Shuheng Zhang, Guoyang Xie +5
Data augmentation is a promising technique for unsupervised anomaly detection in industrial applications, where the availability of positive samples is often limited due to factors…
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…
Augmented Transformers with Adaptive n-grams Embedding for Multilingual Scene Text Recognition
Xueming Yan, Zhihang Fang, Yaochu Jin
While vision transformers have been highly successful in improving the performance in image-based tasks, not much work has been reported on applying transformers to multilingual sc…
A Graph Neural Network with Negative Message Passing for Graph Coloring
Xiangyu Wang, Xueming Yan, Yaochu Jin
Graph neural networks have received increased attention over the past years due to their promising ability to handle graph-structured data, which can be found in many real-world pr…
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
Guoyang Xie, Jinbao Wang, Jiaqi Liu +5
Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently, many advanced algorithms have been reported, but their perfo…
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…
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Guoyang Xie, Jinbao Wang, Jiaqi Liu +2
In the area of fewshot anomaly detection (FSAD), efficient visual feature plays an essential role in memory bank M-based methods. However, these methods do not account for the rela…
Deep Industrial Image Anomaly Detection: A Survey
Jiaqi Liu, Guoyang Xie, Jinbao Wang +4
The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-ba…
Intelligent Computing: The Latest Advances, Challenges and Future
Shiqiang Zhu, Ting Yu, Tao Xu +18
Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm…
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location
Shiqing Liu, Xueming Yan, Yaochu Jin
The facility location problems (FLPs) are a typical class of NP-hard combinatorial optimization problems, which are widely seen in the supply chain and logistics. Many mathematical…
A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm
Qiqi Liu, Yuping Yan, Peter Ligeti +1
Data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving ma…
Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning
Yitian Hong, Yaochu Jin, Yang Tang
In cooperative multi-agent reinforcement learning, centralized training and decentralized execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) decompositio…
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimizat…
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues
Nan Li, Lianbo Ma, Guo Yu +3
Over recent years, there has been a rapid development of deep learning (DL) in both industry and academia fields. However, finding the optimal hyperparameters of a DL model often n…
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…
Towards Fairness-Aware Multi-Objective Optimization
Guo Yu, Lianbo Ma, Wei Du +2
Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. Howev…
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures
Jia Liu, Ran Cheng, Yaochu Jin
Deep neural networks have been found vulnerable to adversarial attacks, thus raising potentially concerns in security-sensitive contexts. To address this problem, recent research h…
Recent Advances in Bayesian Optimization
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the dev…
A Survey on Computationally Efficient Neural Architecture Search
Shiqing Liu, Haoyu Zhang, Yaochu Jin
Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users wit…