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Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization

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arXiv 2022年

作者： Shang, Ke Shu, Tianye Ishibuchi, Hisao Nan, Yang Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

In the evolutionary multi-objective optimization (EMO) field, the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown that the final population often includes solutions which are dominated by other solutions generated and discarded in previous generations. Recently, a new EMO framework has been proposed to solve this issue by storing all the non-dominated solutions generated during the evolution in an archive and selecting a subset of solutions from the archive as the output. The key component in this framework is the subset selection from the archive which usually stores a large number of candidate solutions. However, most studies on subset selection focus on small candidate solution sets for environmental selection. There is no benchmark test suite for large-scale subset selection. This paper aims to fill this research gap by proposing a benchmark test suite for subset selection from large candidate solution sets, and comparing some representative methods using the proposed test suite. The proposed test suite together with the benchmarking studies provides a baseline for researchers to understand, use, compare, and develop subset selection methods in the EMO field. © 2022, CC BY.

关键词： Benchmarking

Multi-objective neural architecture search with almost no training

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arXiv 2020年

作者： Hu, Shengran Cheng, Ran He, Cheng Lu, Zhichao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries. Despite the steady stream of impressive empirical results, most existing NAS algorithms are computationally prohibitive to execute due to the costly iterations of stochastic gradient descent (SGD) training. In this work, we propose an effective alternative, dubbed Random-Weight Evaluation (RWE), to rapidly estimate the performance of network architectures. By just training the last linear classification layer, RWE reduces the computational cost of evaluating an architecture from hours to seconds. When integrated within an evolutionary multi-objective algorithm, RWE obtains a set of efficient architectures with state-of-the-art performance on CIFAR-10 with less than two hours’ searching on a single GPU card. Ablation studies on rank-order correlations and transfer learning experiments to ImageNet have further validated the effectiveness of RWE. © 2020, CC-BY.

关键词： Multiobjective optimization

Learning to approximate: Auto direction vector set generation for hypervolume contribution approximation

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arXiv 2022年

作者： Shang, Ke Shu, Tianye Ishibuchi, Hisao Guangdong Provincial Key Laboratory Of Brain-inspired Intelligent Computation Department Of Computer Science And Engineering Southern University Of Science And Technology Shenzhen518055 China

Hypervolume contribution is an important concept in evolutionary multi-objective optimization (EMO). It involves in hypervolume-based EMO algorithms and hypervolume subset selection algorithms. Its main drawback is that it is computationally expensive in high-dimensional spaces, which limits its applicability to many-objective optimization. Recently, an R2 indicator variant (i.e., R2HVCindicator) is proposed to approximate the hypervolume contribution. The R2HVCindicator uses line segments along a number of direction vectors for hypervolume contribution approximation. It has been shown that different direction vector sets lead to different approximation quality. In this paper, we propose Learning to Approximate (LtA), a direction vector set generation method for the R2HVCindicator. The direction vector set is automatically learned from training data. The learned direction vector set can then be used in the R2HVCindicator to improve its approximation quality. The usefulness of the proposed LtA method is examined by comparing it with other commonly-used direction vector set generation methods for the R2HVCindicator. Experimental results suggest the superiority of LtA over the other methods for generating high quality direction vector sets. © 2022, CC BY.

关键词： Vectors

Lazy Greedy Hypervolume Subset Selection from Large Candidate Solution Sets

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arXiv 2020年

作者： Chen, Weiyu Ishibuchi, Hisao Shang, Ke Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Subset selection is a popular topic in recent years and a number of subset selection methods have been proposed. Among those methods, hypervolume subset selection is widely used. Greedy hypervolume subset selection algorithms can achieve good approximations to the optimal subset. However, when the candidate set is large (e.g., an unbounded external archive with a large number of solutions), the algorithm is very time-consuming. In this paper, we propose a new lazy greedy algorithm exploiting the submodular property of the hypervolume indicator. The core idea is to avoid unnecessary hypervolume contribution calculation when finding the solution with the largest contribution. Experimental results show that the proposed algorithm is hundreds of times faster than the original greedy inclusion algorithm and several times faster than the fastest known greedy inclusion algorithm on many test problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Set theory

Effects of Archive Size on Computation Time and Solution Quality for Multi-Objective Optimization

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arXiv 2022年

作者： Shu, Tianye Shang, Ke Ishibuchi, Hisao Nan, Yang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

An unbounded external archive has been used to store all nondominated solutions found by an evolutionary multiobjective optimization algorithm in some studies. It has been shown that a selected solution subset from the stored solutions is often better than the final population. However, the use of the unbounded archive is not always realistic. When the number of examined solutions is huge, we must pre-specify the archive size. In this study, we examine the effects of the archive size on three aspects: (i) the quality of the selected final solution set, (ii) the total computation time for the archive maintenance and the final solution set selection, and (iii) the required memory size. Unsurprisingly, the increase of the archive size improves the final solution set quality. Interestingly, the total computation time of a medium-size archive is much larger than that of a small-size archive and a huge-size archive (e.g., an unbounded archive). To decrease the computation time, we examine two ideas: periodical archive update and archiving only in later generations. Compared with updating the archive at every generation, the first idea can obtain almost the same final solution set quality using a much shorter computation time at the cost of a slight increase of the memory size. The second idea drastically decreases the computation time at the cost of a slight deterioration of the final solution set quality. Based on our experimental results, some suggestions are given about how to appropriately choose an archiving strategy and an archive size. © 2022, CC BY.

关键词： Deterioration

Power transformer fault diagnosis considering data imbalance and data set fusion

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High Voltage 2021年第3期6卷 543-554页

作者： Yang Zhang Hong Cai Chen Yaping Du Min Chen Jie Liang Jianhong Li Xiqing Fan Xin Yao Department of Building Service Engineering Hong Kong Polytechnic UniversityHong KongChina Academy for Advanced Interdisciplinary Studies and Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina Research Institute State Grid Zhejiang Electric Power Co.LtdHangzhouChina Zhejiang Huayun Information Technology Co. LtdHangzhouChina State Grid Zhejiang Electric Power Co. LtdLishui Power Supply BureauLishuiChina Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina

Improving the accuracy of transformer dissolved gas analysis is always an important demand for power ***,the requirement for large numbers of fault samples becomes an obstacle to this *** article creatively uses a large number of health data,which is much easier to obtain by power companies,to improve diagnosis *** investigations from the view of both data set and methodology to deal with this problem are presented.A data set consists of 9595 health samples and 993 fault samples is used for *** characteristics of the data set and the influence of the health data on diagnostic accuracy are *** performance of many state‐of‐art algorithms that handle the imbalanced prob-lem is ***,an efficient fault diagnosis algorithm named self‐paced ensemble(SPE)is *** SPE,classification hardness is proposed to include the data characteristic in the *** method can guarantee the diversity of the data set and keep high *** to the experiment results,the superior of SPE is confirmed and also proves that involving more health samples can improve transformer diagnosis when fault data are limited.

关键词： handle fault diagnosis

Non-Invasive brain-computer interfaces: a New Perspective on the Assessment and Classification of Individuals with Methamphetamine Addiction

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SN Comprehensive Clinical Medicine 2023年第1期5卷 1-12页

作者： Zhan, Gege Su, Haolong Wang, Pengchao Wang, Junkongshuai Jiang, Haifeng Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Chinese Academy of Sciences Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Yiwu City China

Methamphetamine addiction is a brain disease that causes abnormalities in the structure and function of the brain. EEG, a common signal acquired based on the noninvasive brain-computer interface, can reflect the altered brain activity associated with methamphetamine addiction. EEG-based analysis methods provide a perspective to explore the neural mechanisms of methamphetamine addiction and the effects on brain activity. This paper reports the results of a review of EEG-based assessment and classification of methamphetamine addiction. Current methods commonly used in EEG-based methamphetamine addiction research include traditional resting-state EEG analysis, brain network analysis, and analysis of event-related potentials. A small number of studies have classified methamphetamine addiction and healthy individuals based on resting state EEG features or event-related potentials. EEG is one of the common tools used to examine the effects of methamphetamine on brain function. In follow-up studies, new methods for analyzing resting-state EEG and event-related potentials may help to investigate the neural mechanisms of methamphetamine addiction.

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Gridless evolutionary approach for line spectral estimation with unknown model order

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arXiv 2021年

作者： Yan, Bai Zhao, Qi Zhang, Jin Andrew Zhang, J. Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China University of Technology SydneyNSW2007 Australia

—Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic l0 norm (i.e., the continuous analog of l0 norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of atomic l0 norm, such as nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order by means of the atomic l0 norm. To accomplish this idea, we build a multiobjective optimization model. The measurment error and the atomic l0 norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic l0 norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that help fully exploring the variable and open-ended frequency search space and provide extensive potentials towards the optima. Another innovation is a model order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model order selection. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectrum analysis

DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of computer science & technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network