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SSRN 2024年

作者： Huang, Jianzun Zou, Juan Xia, Yizhang Hou, Zhanglu Liu, Yuan Ye, Songtao Lu, Qifeng Hunan Province ChinaHunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Faculty of School of Computer Science School of Cyberspace Science of Xiangtan University Xiangtan411105 China Xi’an Jiaotong-Liverpool University Taicang Jiangsu Suzhou215400 China

Multi-task neural networks have achieved significant success in various field. However, developing a high-performance multi-task neural network typically necessitates a profound comprehension of multi-task learning principles and the complexities of the real-world problem at hand. In order to facilitate user-friendly multi-task network design, we propose Branched Multi-Task Evolutionary Neural Architecture Search (BMT-ENAS), based on evolutionary computation, to automatically design a network architecture. Specifically, we design a matrix that can be mapped to a branched multi-task architecture and adopt a genetic algorithm to evolve promising matrix individuals in combination with the proposed adaptive weight strategy. Compared with six stateof-the-art competitors, the experimental results illustrate that the BMT-ENAS not only outperforms existing automatic architecture design algorithms in terms of experimental effectiveness but also effectively balances the performance of each task © 2024, The Authors. All rights reserved.

关键词： Network architecture

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An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition

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SSRN 2024年

作者： Xia, Yizhang Xu, Junwen Hou, Zhanglu Song, Shihao Zou, Juan Liu, Yuan Yang, Shengxiang School of Computer Science Xiangtan University Hunan Province Xiangtan411105 China Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China School of Computer Science and Informatics De Montfort University Leicester LeicesterLE1 9BH United Kingdom

Hand gesture recognition (HGR) based on multimodal data has attracted considerable attention due to its potential in various practical applications. Although manually designed multimodal deep networks for multimodal HGR (MHGR) have shown promising results, they often require extensive expertise and time-consuming manual adjustments. To overcome these challenges, we propose an evolutionary network architecture search framework with adaptive multimodal fusion (AMF-ENAS), which automates the design process of multimodal deep networks. Our framework incorporates a novel multimodal fusion strategy that optimizes both the positioning of fusion nodes and the fusion ratios between different branches of the network. It effectively captures the characteristics of data at shallow and deep layers while also considering the varying importance of different data streams. Furthermore, we introduce an encoding strategy for multimodal data adaptation, structuring the encoding space into three functional components: fusion points, fusion ratios, and block selection. This encoding strategy enables flexible customization of the network architecture, with the evolutionary algorithm iteratively searching for optimal configurations across different multimodal datasets. To our knowledge, this is the first application of ENAS in MHGR to directly address the challenges of determining fusion positions and ratios in multimodal data. Experimental results confirm that AMF-ENAS achieves state-of-the-art performance on the Ninapro DB2, DB3, and DB7 datasets. © 2024, The Authors. All rights reserved.

关键词： Gesture recognition

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A Dynamic Multi-Objective Evolutionary Algorithm Based on Niche Prediction Strategy

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SSRN 2022年

作者： Zheng, Jinhua Zhang, Bo Zou, Juan Yang, Shengxiang Hu, Yaru Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

In reality, many multi-objective optimization problems are dynamic. The Pareto optimal front (PF) or Pareto optimal solution (PS) of these dynamic multi-objective problems (DMOPs) changes as the environment change. Therefore, solving such problems requires an optimization algorithm that can quickly track the PF or PS after an environment change. Prediction-based response mechanism is a common method used to deal with environmental changes, which is commonly known as center point-based prediction. However, if the predicted direction of the center point is inaccurate, the predicted population will be biased towards one side. In this paper, we propose a niche prediction strategy based on center and boundary points (PCPB) to solve the dynamic multi-objective optimization problems, which consists of three steps. After environmental changes are detected, the first step is to divide the niche, dividing different individuals in the PS into different niche populations. The second step is to independently predict different niches, and select individuals with good convergence and distribution in the niche to predict the individuals that will produce the next generation. Finally, some different individuals are randomly generated in the next possible PS area to ensure the diversity of the population. To verify whether our proposed strategy is effective and competitive, PCPB was compared with five state-of-the-art strategies. The experimental results show that PCPB performed competitively in solving dynamic multi-objective optimization problems, which proves that our algorithm has good competitiveness. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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