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evolutionary neural architecture search Framework With Masked Encoding Mechanism for Point Cloud Registration

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Wu, Yue Gong, Peiran Yuan, Yongzhe Gong, Maoguo Ma, Wenping Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Inner Mongolia Normal Univ Acad Artificial Intelligence Coll Math Sci Hohhot 010022 Peoples R China Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Deep learning-based point cloud registration (PCR) has received increasing attention in the field of computer vision. However, the lack of expertise in deep neural network architecture design remains a large obstacle for end-users with registration needs. While neural architecture search (NAS) indeed addresses the architecture automation design problem and has undergone substantial development, the majority of existing NAS approaches are tailored for image processing. Because of significant differences in data characteristics and network structures, directly transferring the existing NAS approaches for PCR is infeasible. Hence, this work proposes an evolutionary neural architecture search framework for PCR, which can fit the different types of PCR networks. The framework, equipped with the designed masked encoding mechanism, can seamlessly integrate with the majority of evolutionary computation-based optimization algorithms, thereby reducing the additional cost of operator design. Based on the above framework, the users can freely select the advanced network components and optimization algorithms depending on their needs. Experimental results on synthetic and real-world datasets, ModelNet40 and KITTI, demonstrate the superiority of the proposed framework in terms of registration precision and architecture complexity.

关键词： Feature extraction Point cloud compression Encoding Computer architecture Data mining Accuracy Three-dimensional displays Decoding Transformers neural architecture search Point cloud registration evolutionary neural architecture search novel encoding mechanism

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Score Predictor-Assisted evolutionary neural architecture search

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Jiang, Pengcheng Xue, Yu Neri, Ferrante Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Jiangsu Peoples R China Univ Surrey Sch Comp Sci & Elect Engn NICE Grp Guildford GU2 7XH England

evolutionary neural architecture search (ENAS) treats neural network design as an optimisation problem and addresses it via evolutionary computation. Despite being flexible and enabling automated design, ENAS typically suffers from high computational costs due to the need to train a network at each fitness evaluation. Surrogate-assisted ENAS methods mitigate the severity of this challenge by replacing the computationally expensive fitness function with an approximate computationally cheap fitness function for some fitness evaluations of the run. Currently, a major research challenge in the field is the smooth integration of such surrogate models (and, often, data collection mechanisms) within ENAS frameworks. This paper puts forth a simple yet effective way to address this challenge. During the initial stage of the optimisation, the proposed algorithm, score predictor-assisted ENAS (SPNAS), evolves a small population of candidate architectures using ground truth fitness, i.e., the testing error rate of the network following its training. The data collected in this stage are then used to train a multi-layer perceptron network that builds an alternative fitness function. Unlike algorithms in previous studies, this novel alternative fitness does not approximate the error rate but is designed to preserve its order relation over populations of candidate architectures. Thus, this approach naturally allows for a computationally cheap population ranking. Most of the evolution is then carried out with the surrogate (i.e., alternative) fitness on a large population without retraining the surrogate model or calculating the ground truth fitness. Experiments conducted on the EvoXBench platform show that on its seven search spaces, SPNAS achieves excellent results in terms of error rate despite the modest use of ground-truth fitness calls.

关键词： Computer architecture Training Encoding Predictive models neural architecture search Microprocessors Computational modeling Accuracy Prediction algorithms Graphics processing units evolutionary neural architecture search surrogate model genetic algorithm score prediction

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A Surrogate Model With Multiple Comparisons and Semi-Online Learning for evolutionary neural architecture search

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Xue, Yu Hu, Bohan Neri, Ferrante Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Univ Surrey Sch Comp Sci & Elect Engn NICE Grp Guildford GU2 7XH England

evolutionary neural architecture search (ENAS) refers to techniques to automatically design neural networks through an evolutionary framework. Although ENAS has great potential, its application in practice can be very computationally expensive, especially for the design of Deep neural Networks (DNNs). The use of surrogate models (computationally cheap models that approximate computationally expensive objective functions) is a viable option to reduce the design cost. Research on surrogate models for ENAS has recently shifted from directly predicting the performance of architectures to predicting the outcome of pairwise comparisons. The present paper proposes a novel surrogate-assisted ENAS algorithm, namely a surrogate model with multiple comparisons and semi-online learning (SMCSO). The proposed SMCSO tests the performance of a newly generated architecture against that of a pool of promising architectures (multiple comparisons). This way, the algorithm exhibits robust behaviour, which alleviates the risk of performing wrong selections due to the uncertainty introduced by the surrogate. This logic is then naturally embedded within the particle swarm optimisation (PSO) logic, where newly generated architectures would need to be compared against the local best particle (in our case, there is a pool of local best particles). Furthermore, the initially trained surrogate model is run within the PSO and then retrained every time a prearranged number of local best replacements occur. We call this training technique semi-online learning. It allows us to directly control the trade-off between accuracy and the computational overhead of the surrogate model. Experimental results show that on the NAS-Bench-101 and the NAS-Bench-201, the architectures detected by the proposed SMCSO achieved 94.15% and 94.33% accuracy, respectively, thus, establishing a new state-of-the-art surrogate-assisted ENAS.

关键词： Computer architecture Computational modeling Accuracy Adaptation models Training Convolutional neural networks Predictive models neural architecture search Optimization Encoding evolutionary neural architecture search particle swarm optimisation surrogate model

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evolutionary neural architecture search for Facial Expression Recognition

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第5期7卷 1405-1419页

作者： Deng, Shuchao Lv, Zeqiong Galvan, Edgar Sun, Yanan Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Maynooth Univ Nat Inspired Computat Res Grp Dept Comp Sci Maynooth W23F2K8 Ireland

Facial expression is one of the most powerful, natural, and universal signals for human beings to express emotional states and intentions. There are many applications for facial expression recognition (FER) in human society such as healthcare. Thus, the importance of correct and innovative FER approaches in Artificial Intelligence is evident. However, commonly used methods suffer from a lack of classification generalization in FER. To tackle this problem, we propose a generic facial expression recognition network based on evolutionary neural architecture search, called ENAS-FERNet, which can automatically evolve neural network architectures using both laboratory-controlled and in-the-wild FER datasets. The experiments of ENAS-FERNet were carefully designed and compared with state-of-the-art (SOTA) methods in the case of training from scratch. In addition, we validated the interference resistance of ENAS-FERNet on the synthetic noisy FER dataset and analyzed the time consumption of ENAS-FERNet. Comprehensive experimental analysis and results show that the proposed ENAS-FERNet method achieves the most well-known results on the CK+, Affect-Net, and RAF-DB (10%) datasets, as well as competitive results on the JAFFE, RAF-DB, and RAF-DB (20%) datasets. The results of these experiments show that our ENAS-FERNet has good classification generalization capabilities on these challenging datasets.

关键词： Facial expression recognition evolutionary neural architecture search neural network architectures

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evolutionary neural architecture search for High-Dimensional Skip-Connection Structures on DenseNet Style Networks

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2021年第6期25卷 1118-1132页

作者： O'Neill, Damien Xue, Bing Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

Convolutional neural networks hold state-of-the-art results for image classification, and many neural architecture search algorithms have been proposed to discover high performance convolutional neural networks. However, the use of neural architecture search for the discovery of skip-connection structures, an important element in modern convolutional neural networks, is limited within the literature. Furthermore, while many neural architecture search algorithms utilize performance estimation techniques to reduce computation time, empirical evaluations of these performance estimation techniques remain limited. This work focuses on utilizing evolutionary neural architecture search to examine the search space of networks, which follow a fundamental DenseNet structure, but have no fixed skip connections. In particular, a genetic algorithm is designed, which searches the space consisting of all networks between a standard feedforward network and the corresponding DenseNet. To design the algorithm, lower fidelity performance estimation of this class of networks is examined and presented. The final algorithm finds networks that are more accurate than DenseNets on CIFAR10 and CIFAR100, and have fewer trainable parameters. The structures found by the algorithm are examined to shed light on the importance of different types of skip-connection structures in convolutional neural networks, including the discovery of a simple skip-connection removal, which improves DenseNet performance on CIFAR10.

关键词： Estimation Computer architecture Convolutional neural networks Training Visualization Task analysis Probability distribution Convolutional neural networks (CNNs) evolutionary neural architecture search genetic algorithms neural architecture search (NAS) skip connections

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evolutionary neural architecture search for Traffic Forecasting 21

Evolutionary Neural Architecture Search for Traffic Forecast...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Klosa, Daniel Bueskens, Christof Univ Bremen Ctr Ind Math Bremen Germany

ISBN: (纸本)9781665462839

Traffic forecasting is a challenging task due to complex spatial and temporal dependencies across sensor locations and time. Interest in solving this task has increased, but current research focuses on manually constructing neural network architectures without the aid of neural architecture search (NAS). In our work, we explore evolutionary neural architecture search (ENAS) by deploying a genetic algorithm (GA) to find optimal neural network architectures for predicting traffic conditions. The search space for the GA consists of arbitrary combinations of dilated convolutions and graph convolutions for modelling temporal and spatial dependencies respectively, limited in complexity only by technical constraints. Experimental results show that model architectures obtained via GA are able to match the current state-of-the-art on traffic prediction benchmarks.

关键词： evolutionary neural architecture search genetic algorithm neural architecture search traffic forecasting deep learning

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evolutionary neural architecture search for 2D and 3D Medical Image Classification 24th

Evolutionary Neural Architecture Search for 2D and 3D Medica...

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24th International Conference on Computational Science (ICCS)

作者： Ali, Muhammad Junaid Moalic, Laurent Essaid, Mokhtar Idoumghar, Lhassane Univ Haute Alsace IRIMAS UR 7499 F-68093 Mulhouse France

ISBN: (纸本)9783031637537;9783031637513

Designing deep learning architectures is a challenging and time-consuming task. To address this problem, neural architecture search (NAS) which automatically searches for a network topology is used. While existing NAS methods mainly focus on image classification tasks, particularly 2D medical images, this study presents an evolutionary NAS approach for 2D and 3D Medical image classification. We defined two different search spaces for 2D and 3D datasets and performed a comparative study of different meta-heuristics used in different NAS studies. Moreover, zero-cost proxies have been used to evaluate the performance of deep neural networks, which helps reduce the searching cost of the overall approach. Furthermore, recognizing the importance of Data Augmentation (DA) in model generalization, we propose a genetic algorithm based automatic DA strategy to find the optimal DA policy. Experiments on MedMNIST benchmark and BreakHIS dataset demonstrate the effectiveness of our approach, showcasing competitive results compared to existing AutoML approaches. The source code of our proposed approach is available at https://***/Junaid199f/evo_nas_med_2d_3d.

关键词： evolutionary neural architecture search Medical Image Classification AutoML AutoDL Automatic Data Augmentation

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evolutionary neural architecture search based on evaluation correction and functional units

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KNOWLEDGE-BASED SYSTEMS 2022年 251卷

作者： Shang, Ronghua Zhu, Songling Ren, Jinhong Liu, Hangcheng Jiao, Licheng Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

neural architecture search (NAS) has been a great success in the automated design of deep neural networks. However, neural architecture search using evolutionary algorithms is challenging due to the diverse structure of neural networks and the difficulty in performance evaluation. To this end, this paper proposes an evolutionary neural architecture search algorithm (called EF-ENAS) based on evaluation corrections and functional units. First, a mating selection operation based on evaluation correction is developed, which can help EF-ENAS discriminate high-performance network architectures and reduce the harmful effects of low fidelity accuracy evaluation methods. Then, a functional unit-based network architecture crossover operation is designed, which divides the neural network into different functional units for crossover and protects valuable network architectures from destruction. Finally, the idea of species protection is introduced into the traditional environmental selection operation and a species protection-based environmental selection operation is designed, which can improve the diversity of network architectures in a population. The EF-ENAS is tested on ten benchmark datasets with varying complexities. In addition, the proposed algorithm is compared with 44 state-of-the-art algorithms, including DARTS, EvoCNN, CNN-GA, AE-CNN, etc. The experimental results show that the proposed algorithm1 can automatically design neural networks and perform better. (c) 2022 Elsevier B.V. All rights reserved.

关键词： evolutionary neural architecture search Genetic algorithm Crossover operation Image classification

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An evolutionary neural architecture search for item response theory autoencoders

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Behaviormetrika 2024年 1-24页

作者： Tabak, Gabriel Couto Molenaar, Dylan Curi, Mariana University of São Paulo ICMC São Carlos Brazil Brazilian School of Economics and Finance FGV EPGE Rio de Janeiro Brazil University of Amsterdam Department of Psychology Amsterdam Netherlands

neural networks were previously shown to have advantages in estimating Item Response Theory (IRT) models over more traditional estimation procedures. Specifically, autoencoders, a specific type of neural network, could have adequate estimates in a shorter amount of time. However, some questions arise concerning the architecture of the network. In this article, we explored an evolutionary architecture search within a unidimensional 2-parameter fixed effects autoencoder IRT model. Moreover, we investigated different identification restrictions on the autoencoders. Results indicate that there was no optimal architecture, and many different network configurations were able to reach the same loss value with similar parameter estimates. Also, different restrictions converge to the same loss value, each with its own scale. Regarding accuracy, all the autoencoder methods showed adequate estimates, comparable to traditional IRT methods. When applied a multidimensional autoencoder IRT model to a national exam from Brazil measuring three different domains, results showed that the autoencoder IRT model has similar results as compared to a traditional IRT model estimated using marginal maximum likelihood. © The Behaviormetric Society 2024.

关键词： Autoencoders ENEM evolutionary neural architecture search Item response theory

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Information extraction of Chinese medical electronic records via evolutionary neural architecture search 23

Information extraction of Chinese medical electronic records...

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23rd IEEE International Conference on Data Mining (IEEE ICDM)

作者： Zhang, Tian Li, Nan Zhou, Yuee Cai, Wei Ma, Lianbo Northeastern Univ Software Coll Shenyang Peoples R China Neusoft Res Intelligent Healthcare Technol Co Ltd Shenyang Peoples R China

ISBN: (纸本)9798350381641

To obtain valuable information from the Chinese electronic medical records (CEMR), information extraction (IE), including named entity recognition (NER) and relation extraction (RE), is often used to extract information from semi-structured text. Recently, machine learning (ML) and deep learning (DL) models for IE have been successfully applied to CEMR with positive results. However, the performance of ML and DL relies on extensive expert knowledge and manual parameter tuning. In this paper, we propose a new method based on an evolutionary algorithm to tackle the network structure and training parameters selection problem of BiLSTM for NER and RE. In our method, an efficient mixed-length genetic encoding strategy is designed to represent the BiLSTM network information and the model training parameters. Specifically, all learning rates, num of hidden layers and neurons, and optimizers can be designed automatically, and then an optimal set of selection results can be obtained. In addition, we design efficient genetic operators to adapt to hybrid parameters and propose a deep-learning strategy to reduce computational overhead. The experiments show that our method achieves 89.13% and 60.85% F1 scores on the CCKS2019 Task 1 and CHIP-2020 Task 2 datasets, respectively, which demonstrates the effectiveness of our method.

关键词： information extraction evolutionary neural architecture search Chinese medical electronic records

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