Neural architecture search (NAS) automates neural network design, improving efficiency over manual approaches. However, efficiently discovering high-performance neural network architectures that simultaneously optimize multiple objectives remains a significant challenge in NAS. Existing methods often suffer from limited population diversity and inadequate exploration of the search space, particularly in regions with extreme complexity values. To address these challenges, we propose a multiobjective evolutionary algorithm (MOEA)-BUS, an innovative MOEA based on bipopulation with uniform sampling for NAS, aimed at simultaneously optimizing both accuracy and network complexity. In MOEA-BUS, a novel uniform sampling method is proposed to initialize the population, ensuring that architectures are distributed uniformly across the objective space. Furthermore, to enhance exploration, we deploy a bipopulation framework where two populations evolve synergistically, facilitating comprehensive search space coverage. Experiments on CIFAR-10 and ImageNet demonstrate MOEA-BUS’s superiority, achieving top-one accuracies of 98.39% on CIFAR-10, and 80.03% on ImageNet. Notably, it achieves 78.28% accuracy on ImageNet with only 446 M MAdds. Ablation studies confirm that both uniform sampling and bipopulation mechanisms enhance population diversity and performance. In addition, in terms of Kendall’s tau coefficient, the SVM achieves an improvement of at least 0.035 compared to the other three commonly used machine learning models, and uniform sampling provided an enhancement of approximately 0.07.

Neural architecture search (NAS) automates neural network design, improving efficiency over manual approaches. However, efficiently discovering high-performance neural network architectures that simultaneously optimize multiple objectives remains a significant challenge in NAS. Existing methods often suffer from limited population diversity and inadequate exploration of the search space, particularly in regions with extreme complexity values. To address these challenges, we propose MOEA-BUS, an innovative multi-objective evolutionary algorithm based on bi-population with uniform sampling for neural architecture search, aimed at simultaneously optimizing both accuracy and network complexity. In MOEA-BUS, a novel uniform sampling method is proposed to initialize the population, ensuring that architectures are distributed uniformly across the objective space. Furthermore, to enhance exploration, we deploy a bi-population framework where two populations evolve synergistically, facilitating comprehensive search space coverage. Experiments on CIFAR-10 and ImageNet demonstrate MOEA-BUS's superiority, achieving top-1 accuracies of 98.39

Neural architecture search (NAS) has emerged as a powerful paradigm that enables researchers to automatically explore vast search spaces and discover efficient neural networks. However, NAS suffers from a critical bottleneck, i.e., the evaluation of numerous architectures during the search process demands substantial computing resources and time. In order to improve the efficiency of NAS, a series of methods have been proposed to reduce the evaluation time of neural architectures. However, they are not efficient enough and still only focus on the accuracy of architectures. Beyond classification accuracy, real-world applications increasingly demand more efficient and compact network architectures that balance multiple performance criteria. To address these challenges, we propose the SMEMNAS, a pairwise comparison relation-assisted multiobjective evolutionary algorithm (EA) based on a multipopulation (MP) mechanism. In the SMEMNAS, a surrogate model is constructed based on pairwise comparison relations to predict the accuracy ranking of architectures, rather than the absolute accuracy. Moreover, two populations cooperate with each other in the search process, i.e., a main population that guides the evolutionary process and a vice population that enhances search diversity. Our method aims to discover high-performance models that simultaneously optimize multiple objectives. We conduct comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets to validate the effectiveness of our approach. With only a single GPU searching for 0.17 days, competitive architectures can be found by SMEMNAS, which achieves 78.91% accuracy with the MAdds of 570 M on the ImageNet. This work makes a significant advancement in the field of NAS.

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.

Evolutionary neural architecture search (ENAS) automates the design of high-performing neural networks but is often hindered by the high computational cost of evaluating individual architectures. Surrogate models mitigate this issue by predicting performance, yet their accuracy depends on the quality of training data and their ability to utilise insights from real evaluations. This paper presents homogeneous encoding-based ENAS (HENAS), a novel method addressing these challenges through two key innovations: homogeneous architecture augmentation and confidence-based prediction. Through homogeneous architecture augmentation, HENAS exploits redundant encodings in the MobileNetV3 search space to generate multiple representations of the same architecture, enhancing the surrogate model’s training data without additional cost. Confidence-based prediction introduces a mechanism to identify architectures with uncertain performance estimates, prioritising them for evaluation. Integrated into an evolutionary framework, these techniques improve search efficiency and exploration. Experiments on CIFAR-10, CIFAR-100, and ImageNet show that HENAS achieves state-of-the-art performance with reduced computational expense. Ablation studies confirm the contributions of its core components, highlighting the value of redundancy exploitation and uncertainty management in surrogate-assisted ENAS.

Generative Adversarial Networks (GANs) have demonstrated remarkable success in high-quality image synthesis, with StyleGAN and its successor, StyleGAN2, achieving state-of-the-art performance in terms of realism and control over generated features. However, the large number of parameters and high floating-point operations per second (FLOPs) hinder real-time applications and scalability, posing challenges for deploying these models in resource-constrained environments such as edge devices and mobile platforms. To address this issue, we propose Evolutionary Channel Pruning for StyleGANs (ECP-StyleGANs), a novel algorithm that leverages evolutionary algorithms to compress StyleGAN and StyleGAN2 while maintaining competitive image quality. Our approach encodes pruning configurations as binary masks on the model's convolutional channels and iteratively refines them through selection, crossover, and mutation. By integrating carefully designed fitness functions that balance model complexity and generation quality, ECP-StyleGANs identifies optimally pruned architectures that reduce computational demands without compromising visual fidelity, achieving approximately a 4 × reduction in FLOPs and parameters, while maintaining visual fidelity with only a slight increase in FID (Fréchet Inception Distance) compared to the original un-pruned model. This study should be interpreted as a preliminary step towards the formulation and management of the generative AI pruning problem as a multi-objective optimisation task, aimed at enhancing the trade-off between model efficiency and image quality, thereby making large deep models more accessible for real-world applications such as edge devices and resource-constrained environments. Source codes will be available.

In many modern machine learning (ML) models, attention mechanisms (AMs) play a crucial role in processing data and identifying significant parts of the inputs, whether these are text or images. This selective focus enables subsequent stages of the model to achieve improved classification performance. Traditionally, AMs are applied as a preprocessing substructure before a neural network, such as in encoder/decoder architectures. In this paper, we extend the application of AMs to intermediate stages of data propagation within ML models. Specifically, we propose a generalized attention mechanism (GAM), which can be integrated before each layer of a neural network for classification tasks. The proposed GAM allows for at each layer/step of the ML architecture identification of the most relevant sections of the intermediate results. Our experimental results demonstrate that incorporating the proposed GAM into various ML models consistently enhances the accuracy of these models. This improvement is achieved with only a marginal increase in the number of parameters, which does not significantly affect the training time.

Neural architecture search (NAS) is an increasingly popular method for the automatic design of neural networks. Although promising, NAS is often associated with a significant computational cost. Surrogate models, which predict the performance of candidate networks without training them, are thus used to speed up NAS calculations. Since surrogate models must be trained, their performance depends on the dataset of labelled candidate architectures. The generation of these samples can be time-consuming as it requires the training of an architecture. The present paper proposes an inexpensive way of generating training data for the surrogate model. Specifically, the proposed algorithm makes use of isomorphism to obtain more training data for the graph-based encoding. We propose an isomorphic training which combines the use of the Mean Squared Error (MSE) with a novel isomorphic loss function. Then, we propose an isomorphic score to predict the performance of candidate architectures. The proposed isomorphic-based surrogate is integrated within an evolutionary framework for NAS. Numerical experiments are performed on NAS-Bench101 and NAS-Bench201 search spaces. The experimental results demonstrate that the proposed Isomorphic Training and Prediction Evolutionary Neural Architecture Search (ITP-ENAS) algorithm can identify architectures with better performance than other state-of-the-art algorithms, despite training only 424 architectures.

Deep convolutional neural networks (CNNs) are widely used for image classification. Deep CNNs often require a large memory and abundant computation resources, limiting their usability in embedded or mobile devices. To overcome this limitation, several pruning methods have been proposed. However, most of the existing methods focus on pruning parameters and cannot efficiently address the computation costs of deep CNNs. Additionally, these methods ignore the connections between the feature maps of different layers. This paper proposes a multi-objective pruning based on feature map selection (MOP-FMS). Unlike previous studies, we use the number of floating point operations (FLOPs) as a pruning objective in addition to the accuracy of the pruned network. First, we propose an encoding method based on feature map selection with a compact and efficient search space. Second, novel domain-specific crossover and mutation operators with reparation are designed to generate new individuals and make them meet the constraint rules. Then, decoding and pruning methods are proposed to prune networks based on the results of feature map selection. Finally, multi-objective optimisation is used for evaluation and individual selection. Our method has been tested with commonly used network structures. Numerical results demonstrate that the proposed method achieves better results than other state-of-the-art methods in terms of pruning rate without decreasing the accuracy rate to a high degree.

Dropout is an effective method of mitigating over-fitting while training deep neural networks (DNNs). This method consists of switching off (dropping) some of the neurons of the DNN and training it by keeping the remaining neurons active. This approach makes the DNN general and resilient to changes in its inputs. However, the probability of a neuron belonging to a layer to be dropped, the 'dropout rate', is a hard-to-tune parameter that affects the performance of the trained model. Moreover, there is no reason, besides being more practical during parameter tuning, why the dropout rate should be the same for all neurons across a layer. This paper proposes a novel method to guide the dropout rate based on an evolutionary algorithm. In contrast to previous studies, we associate a dropout with each individual neuron of the network, thus allowing more flexibility in the training phase. The vector encoding the dropouts for the entire network is interpreted as the candidate solution of a bi-objective optimisation problem, where the first objective is the error reduction due to a set of dropout rates for a given data batch, while the second objective is the distance of the used dropout rates from a pre-arranged constant. The second objective is used to control the dropout rates and prevent them from becoming too small, hence ineffective; or too large, thereby dropping a too-large portion of the network. Experimental results show that the proposed method, namely GADropout, produces DNNs that consistently outperform DNNs designed by other dropout methods, some of them being modern advanced dropout methods representing the state-of-the-art. GADroput has been tested on multiple datasets and network architectures.

With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.

松软场地上核岛结构的地震安全正成为一个挑战性的重大工程问题.选取代表性的近场强震、中远场强震和远场大震记录作为基岩地震动,考虑场地工程地质特性、土体非线性和人工边界条件,对松软场地-桩筏基础-AP1000核岛结构三维体系进行非线性地震反应分析,结果表明:核岛结构谱加速度的卓越周期与基岩地震动的基本相同,对与核岛主体结构基本频率相近的地震动分量的反应更为强烈,冷却系统水箱"晃动水"的地震反应对核岛结构的影响类似于鞭梢效应;核岛结构的峰值加速度放大系数随结构高度增大,近场和远场强震作用时,该放大效应主要取决于核岛结构自身特性及基岩地震动经土层到核岛底部的传播;核岛结构相对其底部的峰值相对位移随结构高度增大,且远场大震作用时的反应更为强烈;随基岩峰值加速度的增大,核岛结构的峰值加速度放大效应减弱、峰值相对位移反应增大.地基柔性及土-结构相互作用的耦合效应对输入地震动具有高频过滤、低频放大效应,从而使核岛结构下部的峰值加速度显著放大、中上部的峰值加速度显著减小.

隧道掘进爆破施工威胁临近建筑物的安全,尤其是结构性能日益衰退的古建筑.为降低此类爆破施工的环境影响,结合南京地铁4号线鼓楼站爆破施工现场模拟试验,对比分析了单孔爆破和多孔孔间毫秒延迟爆破对省级木结构保护文物鼓楼的振动安全影响.根据现场地质资料,模拟爆破点选在离古建筑距离较远而地质条件相近的拟建风道口处.根据6种爆破强度的模拟结果,研究了地表振动衰减和古建筑振动响应规律,并以此预估实际爆破施工时古建筑的峰值振动速度.试验结果表明:2种爆破方式均未造成古建筑鼓楼的进一步损伤,但相比于单孔装药量为500 g的爆破结果,总装药量为1100 g的6孔毫秒延时爆破能显著地减小峰值振速.文物鼓楼处地表的水平和竖直向振动衰减规律均符合萨道夫斯基公式,水平振动占主导地位;单孔爆破400 g装药量下共振效应最为显著,速度放大效应约为2.4倍.

为探究循环预剪对饱和粉土力学性质的影响,本文系统开展了多循环加载历史对饱和粉土不排水剪切特性影响的试验研究.利用空心圆柱扭剪仪,针对饱和粉土(Dr=50%)进行了不同循环应力比和循环加载方式的循环预剪和二次固结静剪试验.试验结果发现:随着液化水平的提高,饱和粉土重固结所产生的体应变逐渐增大,应变硬化逐渐增强,相变强度SPT 逐渐提高,但相变有效内摩擦角变化不大.在围压不变的前提下,体应变、应力应变关系、有效应力路径、相变强度SPT 以及相变有效内摩擦角几乎与前期加载的循环应力比和循环加载方式无关.本文研究对粉土地区的工程建设具有实际参考价值.

为了研究某核电工程桩筏基础抗震性能,设计缩尺筏基振动台实验.试验模型使用ABAQUS软件进行数值模拟以验证试验设计方案的可行性.依托该实际工程,实验缩尺比例为1/200.地震波选用国内外核电厂结构相关技术文件和规范规定,采用R.G1.60标准反应谱拟合的人工波进行输入计算.结果表明:桩间土输出加速度越往模型上部越大;桩顶部位移比桩低部大;筏板加速度与位移均小于桩体.分析成果可为振动台试验的改进提供指导并为实际工程设计提供参考.

近年来,二维码作为一种存储与传递信息的新技术被应用于很多领域,例如共享单车、安全支付等.在扫描过程中,二维码作为信息媒介与身份认证的工具发挥了重要作用,但因二维码广泛使用带来的安全问题也层出不穷.因此,在保证二维码可用性的基础上,需提高二维码的安全性并对其进行美化,使其图案具有一定防伪能力.结合二维码理论,使用安全哈希算法对其内容进行加密,并采用像素替换法使二维码具有较好的美观性与视觉辨识度.扫描时通过二值化算法,并结合灰度化处理,从而获取二维码信息.实验结果表明,该方案能达到较好的防伪效果,并能保持一般二维码具有的最大内容容量.

在图像加密技术领域中,已存在多种图像加密方法,但很多图像加密方法因安全性不够而存在被破解的风险。现行的很多密文图像安全性评判方法,选择的参数较为单一,评价结果不够全面。对此,提出一种基于像素数量改变率、密钥空间大小、信息熵、相邻像素相关系数等多种指标参数的密文图像安全性评价方案。通过同一图像使用不同加密算法分别得到的多种指标参数横向比较,得出综合情况下安全性更好的算法。针对现阶段加密应用中使用较多的两类图像进行实验、分析和对比,结果表明方案非常有效、可靠、实用性强。