no code implementations • 16 Apr 2024 • Maojiang Tian, Mingke Chen, Wei Du, Yang Tang, Yaochu Jin
In this article, we propose a two-stage enhanced grouping method for large-scale overlapping problems, called OEDG, which achieves accurate grouping while significantly reducing computational resource consumption.
no code implementations • 2 Mar 2024 • Maojiang Tian, Minyang Chen, Wei Du, Yang Tang, Yaochu Jin, Gary G. Yen
Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a non-separable variable grouping method.
no code implementations • 4 Feb 2024 • Wenxuan Fang, Wei Du, Renchu He, Yang Tang, Yaochu Jin, Gary G. Yen
The presence of nonlinearity, integer constraints, and a large number of decision variables adds complexity to this problem, posing challenges for traditional and evolutionary algorithms.
no code implementations • 29 Jan 2024 • Xueming Yan, Yaochu Jin
Evolutionary algorithms have been successful in solving multi-objective optimization problems (MOPs).
no code implementations • 15 Jan 2024 • Fei Ming, Wenyin Gong, Ling Wang, Yaochu Jin
By using a Q-Network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.
no code implementations • 13 Jan 2024 • Peng Yue, Yaochu Jin, Xuewu Dai, ZhenHua Feng, Dongliang Cui
Train timetable rescheduling (TTR) aims to promptly restore the original operation of trains after unexpected disturbances or disruptions.
no code implementations • 9 Jan 2024 • Wanting Zhang, Wei Du, Guo Yu, Renchu He, Wenli Du, Yaochu Jin
On the basis of the proposed model, a dual-stage evolutionary algorithm driven by heuristic rules (denoted by DSEA/HR) is developed, where the dual-stage search mechanism consists of global search and local refinement.
no code implementations • 2 Jan 2024 • Wei Du, Wenxuan Fang, Chen Liang, Yang Tang, Yaochu Jin
The primary objective of the peak-detection stage is to identify peaks in the fitness landscape of the original optimization problem.
no code implementations • 30 Dec 2023 • Yaqing Hou, Mingyang Sun, Abhishek Gupta, Yaochu Jin, Haiyin Piao, Hongwei Ge, Qiang Zhang
In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function).
no code implementations • 10 Oct 2023 • Shiqing Liu, Xueming Yan, Yaochu Jin
It has been shown that learning-based methods outperform traditional heuristics and mathematical solvers on the Traveling Salesman Problem (TSP) in terms of both performance and computational efficiency.
1 code implementation • 24 Aug 2023 • Mai Peng, Zeneng She, Delaram Yazdani, Danial Yazdani, Wenjian Luo, Changhe Li, Juergen Branke, Trung Thanh Nguyen, Amir H. Gandomi, Yaochu Jin, Xin Yao
In this paper, to assist researchers in performing experiments and comparing their algorithms against several EDOAs, we develop an open-source MATLAB platform for EDOAs, called Evolutionary Dynamic Optimization LABoratory (EDOLAB).
1 code implementation • 10 Jul 2023 • Shangshang Yang, Haiping Ma, Cheng Zhen, Ye Tian, Limiao Zhang, Yaochu Jin, Xingyi Zhang
Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability.
no code implementations • 10 Jul 2023 • Guoyang Xie, Jinbao Wang, Yawen Huang, Jiayi Lyu, Feng Zheng, Yefeng Zheng, Yaochu Jin
To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty.
no code implementations • 14 Jun 2023 • Felix Lanfermann, Qiqi Liu, Yaochu Jin, Sebastian Schmitt
In this study we focus on utilizing the concept identification technique for finding relevant and viable energy management configurations from a very large data set of Pareto-optimal solutions.
no code implementations • 3 Jun 2023 • Tao Lei, Yetong Xu, Hailong Ning, Zhiyong Lv, Chongdan Min, Yaochu Jin, Asoke K. Nandi
Popular Transformer networks have been successfully applied to remote sensing (RS) image change detection (CD) identifications and achieve better results than most convolutional neural networks (CNNs), but they still suffer from two main problems.
no code implementations • 6 Apr 2023 • Lingrui Zhang, Shuheng Zhang, Guoyang Xie, Jiaqi Liu, Hua Yan, Jinbao Wang, Feng Zheng, Yaochu Jin
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 such as commercial competition and sample collection difficulties.
1 code implementation • IEEE Transactions on Geoscience and Remote Sensing 2023 • Tao Lei, Xinzhe Geng, Hailong Ning, Zhiyong Lv, Maoguo Gong, Yaochu Jin, Asoke K. Nandi
First, the existing multiscale feature fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage.
Ranked #2 on Change Detection on DSIFN-CD
Building change detection for remote sensing images Change Detection +1
no code implementations • 7 Mar 2023 • Hui Bai, Ran Cheng, Yaochu Jin
This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL).
no code implementations • 28 Feb 2023 • 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 scene text recognition due to the complexities in the visual appearance of multilingual texts.
2 code implementations • 31 Jan 2023 • Guoyang Xie, Jinbao Wang, Jiaqi Liu, Jiayi Lyu, Yong liu, Chengjie Wang, Feng Zheng, Yaochu Jin
We realize that the lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications.
1 code implementation • 29 Jan 2023 • Beichen Huang, Ran Cheng, Zhuozhao Li, Yaochu Jin, Kay Chen Tan
Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence.
no code implementations • 28 Jan 2023 • Guoyang Xie, Jinbao Wang, Jiaqi Liu, Feng Zheng, Yaochu Jin
Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and can improve the performance of anomaly detection.
1 code implementation • 27 Jan 2023 • Jiaqi Liu, Guoyang Xie, Jinbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, Yaochu Jin
In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
no code implementations • 26 Jan 2023 • Xiangyu Wang, Xueming Yan, Yaochu Jin
In this paper, we propose a graph network model for graph coloring, which is a class of representative heterophilous problems.
1 code implementation • CVPR 2023 • Peng Liao, Yaochu Jin, Wenli Du
In deep learning, this is usually achieved by sharing a common neural network architecture and jointly training the weights.
no code implementations • 21 Nov 2022 • Shiqiang Zhu, Ting Yu, Tao Xu, Hongyang Chen, Schahram Dustdar, Sylvain Gigan, Deniz Gunduz, Ekram Hossain, Yaochu Jin, Feng Lin, Bo Liu, Zhiguo Wan, Ji Zhang, Zhifeng Zhao, Wentao Zhu, Zuoning Chen, Tariq Durrani, Huaimin Wang, Jiangxing Wu, Tongyi Zhang, Yunhe Pan
In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.
no code implementations • 27 Oct 2022 • Shiqing Liu, Xueming Yan, Yaochu Jin
The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively.
no code implementations • 15 Oct 2022 • Qiqi Liu, Yuping Yan, Peter Ligeti, Yaochu Jin
To address this issue, this paper proposes a secure federated data-driven evolutionary multi-objective optimization algorithm to protect both the raw data and the newly infilled solutions obtained by optimizing the acquisition function conducted on the server.
no code implementations • 25 Aug 2022 • Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
To this end, we develop a multi-objective Bayesian evolutionary optimization approach to HE-MOPs by exploiting the different data sets on the cheap and expensive objectives in HE-MOPs to alleviate the search bias caused by the heterogeneous evaluation costs for evaluating different objectives.
no code implementations • 23 Aug 2022 • Nan Li, Lianbo Ma, Guo Yu, Bing Xue, Mengjie Zhang, Yaochu Jin
Specifically, we firstly illuminate EDL from machine learning and EC and regard EDL as an optimization problem.
1 code implementation • 8 Aug 2022 • Zhichao Lu, Ran Cheng, Yaochu Jin, Kay Chen Tan, Kalyanmoy Deb
From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them.
no code implementations • 22 Jul 2022 • Guo Yu, Lianbo Ma, Wei Du, Wenli Du, Yaochu Jin
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.
no code implementations • 12 Jul 2022 • Jia Liu, Ran Cheng, Yaochu Jin
First, we formulate the NAS problem for enhancing adversarial robustness of deep neural networks into a multiobjective optimization problem.
no code implementations • 7 Jun 2022 • Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency.
no code implementations • 3 Jun 2022 • 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 without rich expertise to benefit from the success of deep neural networks (DNNs).
1 code implementation • 14 Feb 2022 • Xi Jiang, Guoyang Xie, Jinbao Wang, Yong liu, Chengjie Wang, Feng Zheng, Yaochu Jin
In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies.
no code implementations • 14 Feb 2022 • Foivos Ntelemis, Yaochu Jin, Spencer A. Thomas
Specifically, the prior transformation functions are replaced with a self-transformation mechanism, derived through an unsupervised training process of adversarial training, for imposing invariant representations.
no code implementations • 14 Feb 2022 • Guoyang Xie, Yawen Huang, Jinbao Wang, Jiayi Lyu, Feng Zheng, Yefeng Zheng, Yaochu Jin
This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks.
1 code implementation • 29 Jan 2022 • Jinbao Wang, Guoyang Xie, Yawen Huang, Yefeng Zheng, Yaochu Jin, Feng Zheng
The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms.
1 code implementation • 22 Jan 2022 • Jinbao Wang, Guoyang Xie, Yawen Huang, Jiayi Lyu, Yefeng Zheng, Feng Zheng, Yaochu Jin
There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions.
no code implementations • 29 Sep 2021 • Rui Wang, Oğuzhan Ersoy, Hangyu Zhu, Yaochu Jin, Kaitai Liang
Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without revealing private local information.
no code implementations • 30 Aug 2021 • Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time.
no code implementations • 25 Aug 2021 • Hangyu Zhu, Rui Wang, Yaochu Jin, Kaitai Liang
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data.
1 code implementation • 22 Jun 2021 • Jinjin Xu, Yaochu Jin, Wenli Du
Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization.
no code implementations • 12 Jun 2021 • Hangyu Zhu, Jinjin Xu, Shiqing Liu, Yaochu Jin
Federated learning is an emerging distributed machine learning framework for privacy preservation.
no code implementations • 22 May 2021 • Ye Tian, Xingyi Zhang, Cheng He, Kay Chen Tan, Yaochu Jin
In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems.
1 code implementation • 12 Mar 2021 • Foivos Ntelemis, Yaochu Jin, Spencer A. Thomas
Specifically, the proposed clustering objective employs mutual information, and maximizes the dependency between the integrated discrete representation and a discrete probability distribution.
Ranked #2 on Image Clustering on Tiny-ImageNet
no code implementations • 28 Feb 2021 • Guoyang Xie, Jinbao Wang, Guo Yu, Feng Zheng, Yaochu Jin
Our work focuses on how to improve the robustness of tiny neural networks without seriously deteriorating of clean accuracy under mobile-level resources.
1 code implementation • 16 Feb 2021 • Jinjin Xu, Yaochu Jin, Wenli Du, Sai Gu
Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems.
no code implementations • 16 Jan 2021 • Jia Liu, Yaochu Jin
Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans.
no code implementations • 10 Jan 2021 • Yan Xiao, Yaochu Jin, Kuangrong Hao
First, based on the prototypical networks, we propose an adaptive mixture mechanism to add label words to the representation of the class prototype, which, to the best of our knowledge, is the first attempt to integrate the label information into features of the support samples of each class so as to get more interactive class prototypes.
no code implementations • 14 Dec 2020 • Cuie Yang, Jinliang Ding, Yaochu Jin, Tianyou Chai
Existing work on data-driven optimization focuses on problems in static environments, but little attention has been paid to problems in dynamic environments.
1 code implementation • 8 Nov 2020 • Foivos Ntelemis, Yaochu Jin, Spencer A. Thomas
Image clustering has recently attracted significant attention due to the increased availability of unlabelled datasets.
no code implementations • 12 Sep 2020 • Hangyu Zhu, Haoyu Zhang, Yaochu Jin
Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern.
Distributed, Parallel, and Cluster Computing
1 code implementation • 18 Jul 2020 • Zhihua Liu, Lei Tong, Zheheng Jiang, Long Chen, Feixiang Zhou, Qianni Zhang, Xiangrong Zhang, Yaochu Jin, Huiyu Zhou
Brain tumor segmentation is one of the most challenging problems in medical image analysis.
no code implementations • 27 Mar 2020 • Thiago Zafalon Miranda, Diorge Brognara Sardinha, Márcio Porto Basgalupp, Yaochu Jin, Ricardo Cerri
Recently, the interest in interpretable classification models has grown, partially as a consequence of regulations such as the General Data Protection Regulation.
no code implementations • 10 Mar 2020 • Yan Xiao, Yaochu Jin, Ran Cheng, Kuangrong Hao
With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information.
no code implementations • 7 Mar 2020 • Haoyu Zhang, Yaochu Jin, Ran Cheng, Kuangrong Hao
Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms.
1 code implementation • 7 Mar 2020 • Jinjin Xu, Wenli Du, Ran Cheng, Wangli He, Yaochu Jin
Learning over massive data stored in different locations is essential in many real-world applications.
no code implementations • 4 Mar 2020 • Hangyu Zhu, Yaochu Jin
Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning.
no code implementations • 23 Dec 2019 • Sampo Kuutti, Richard Bowden, Yaochu Jin, Phil Barber, Saber Fallah
However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios.
no code implementations • 14 Nov 2019 • Yushuai Hu, Yaochu Jin, Runhua Li, Xiangxiang Zhang
Accurately locating the start and end time of an action in untrimmed videos is a challenging task.
no code implementations • 20 Oct 2019 • Yu Chen, Yuanyuan Yang, Yaochu Jin, Xiufen Zou
Motivation: Identifying interaction clusters of large gene regulatory networks (GRNs) is critical for its further investigation, while this task is very challenging, attributed to data noise in experiment data, large scale of GRNs, and inconsistency between gene expression profiles and function modules, etc.
no code implementations • 11 Oct 2019 • Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin
The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables.
no code implementations • 10 Jul 2019 • Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin
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 highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i. e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.
no code implementations • 18 Mar 2019 • Yang Chen, Xiaoyan Sun, Yaochu Jin
The proposed algorithm is empirically on two datasets with different deep neural networks.
no code implementations • 18 Dec 2018 • Hangyu Zhu, Yaochu Jin
A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks.
no code implementations • 28 Aug 2018 • Bin Liu, Yaochu Jin
To this end, we make an effort to extend the scope of application of the PFO paradigm to multi-objective optimization (MOO) cases.
no code implementations • 8 Jun 2017 • Yuan Yuan, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Bingshui Da, Qingfu Zhang, Kay Chen Tan, Yaochu Jin, Hisao Ishibuchi
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.
no code implementations • 20 Feb 2017 • Joseph Chrol-Cannon, Yaochu Jin, André Grüning
This work presents a new model of polychronous patterns that can capture precise sequences of spikes directly in the neural simulation.
no code implementations • 4 Jan 2017 • Ye Tian, Ran Cheng, Xingyi Zhang, Yaochu Jin
To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators.