IEEE Transactions on Emerging Topics in Computational Intelligence最新文献

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors IEEE计算智能信息新主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-09-24 DOI: 10.1109/TETCI.2025.3607161

引用次数: 0

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-09-24 DOI: 10.1109/TETCI.2025.3607159

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Contrastive Learning-Based Agent Modeling for Deep Reinforcement Learning 基于对比学习的深度强化学习智能体建模

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-08-13 DOI: 10.1109/TETCI.2025.3595684

Wenhao Ma;Yu-Chen Chang;Jie Yang;Yu-Kai Wang;Chin-Teng Lin

{"title":"Contrastive Learning-Based Agent Modeling for Deep Reinforcement Learning","authors":"Wenhao Ma;Yu-Chen Chang;Jie Yang;Yu-Kai Wang;Chin-Teng Lin","doi":"10.1109/TETCI.2025.3595684","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3595684","url":null,"abstract":"Multi-agent systems often require agents to collaborate with or compete against other agents with diverse goals, behaviors, or strategies. Agent modeling is essential when designing adaptive policies for intelligent machine agents in multi-agent systems, as this is the means by which the controlled agent (ego agent) understands other agents' (modeled agents) behavior and extracts their meaningful policy representations. These representations can be used to enhance the ego agent's adaptive policy which is trained by reinforcement learning. However, existing agent modeling approaches typically assume the availability of local observations from modeled agents during training or a long observation trajectory for policy adaption. To remove these constrictive assumptions and improve agent modeling performance, we devised a <bold>Contrastive <bold>Learning-based <bold>Agent <bold>Modeling (<bold>CLAM) method that relies only on the local observations from the ego agent during training and execution. With these observations, CLAM is capable of generating consistent high-quality policy representations in real time right from the beginning of each episode. We evaluated the efficacy of our approach in both cooperative and competitive multi-agent environments. The experiment results demonstrate that our approach improves reinforcement learning performance by at least 28% on cooperative and competitive tasks, which exceeds the state-of-the-art.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 5","pages":"3719-3726"},"PeriodicalIF":5.3,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-07-23 DOI: 10.1109/TETCI.2025.3586976

引用次数: 0

IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors IEEE计算智能信息新主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-07-23 DOI: 10.1109/TETCI.2025.3586974

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IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information IEEE计算智能新兴主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-07-23 DOI: 10.1109/TETCI.2025.3586972

引用次数: 0

AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms With Diverse Structures AutoOpt：一种自动设计不同结构元启发式优化算法的通用框架

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-04-28 DOI: 10.1109/TETCI.2025.3561629

Qi Zhao;Bai Yan;Taiwei Hu;Xianglong Chen;Jian Yang;Shi Cheng;Yuhui Shi

{"title":"AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms With Diverse Structures","authors":"Qi Zhao;Bai Yan;Taiwei Hu;Xianglong Chen;Jian Yang;Shi Cheng;Yuhui Shi","doi":"10.1109/TETCI.2025.3561629","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3561629","url":null,"abstract":"Metaheuristics are widely recognized gradient-free solvers to hard problems that do not meet the rigorous mathematical assumptions of conventional solvers. The automated design of metaheuristic algorithms provides an attractive path to relieve manual design effort and gain enhanced performance beyond human-made algorithms. However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family. To address this challenge, this paper proposes a general framework, AutoOpt, for automatically designing metaheuristic algorithms with diverse structures. AutoOpt contains three innovations: (i) A general algorithm prototype dedicated to covering the metaheuristic family as widely as possible. It promotes high-quality automated design on different problems by fully discovering potentials and novelties across the family. (ii) A directed acyclic graph algorithm representation to fit the proposed prototype. Its flexibility and evolvability enable discovering various algorithm structures in a single run of design, thus boosting the possibility of finding high-performance algorithms. (iii) A graph representation embedding method offering an alternative compact form of the graph to be manipulated, which ensures AutoOpt's generality. Experiments on numeral functions and real applications validate AutoOpt's efficiency and practicability.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 5","pages":"3690-3703"},"PeriodicalIF":5.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-Sampling From a Single Image SG-GAN：从单幅图像上采样3D脑点云的精细立体感知生成

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-04-21 DOI: 10.1109/TETCI.2025.3558447

Bowen Hu;Weiheng Yao;Sibo Qiao;Hieu Pham;Shuqiang Wang;Michael Kwok-Po Ng

{"title":"SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-Sampling From a Single Image","authors":"Bowen Hu;Weiheng Yao;Sibo Qiao;Hieu Pham;Shuqiang Wang;Michael Kwok-Po Ng","doi":"10.1109/TETCI.2025.3558447","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3558447","url":null,"abstract":"In minimally-invasive brain surgeries with indirect and narrow operating environments, 3D brain reconstruction is crucial. However, as requirements of accuracy for some new minimally-invasive surgeries (such as brain-computer interface surgery) are higher and higher, the outputs of conventional 3D reconstruction, such as point cloud (PC), are facing the challenges that sample points are too sparse and the precision is insufficient. On the other hand, there is a scarcity of high-density point cloud datasets, which makes it challenging to train models for direct reconstruction of high-density brain point clouds. In this work, a novel model named stereoscopic-aware graph generative adversarial network (SG-GAN) with two stages is proposed to generate fine high-density PC conditioned on a single image. The Stage-I GAN sketches the primitive shape and basic structure of the organ based on the given image, yielding Stage-I point clouds. The Stage-II GAN takes the results from Stage-I and generates high-density point clouds with detailed features. The Stage-II GAN is capable of correcting defects and restoring the detailed features of the region of interest (ROI) through the up-sampling process. Furthermore, a parameter-free-attention-based free-transforming module is developed to learn the efficient features of input, while upholding a promising performance. Comparing with the existing methods, the SG-GAN model shows superior performance in terms of visual quality, objective measurements, and performance in classification, as demonstrated by comprehensive results measured by several evaluation metrics including PC-to-PC error and Chamfer distance.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 5","pages":"3677-3689"},"PeriodicalIF":5.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Neural Networks and Calibration Measures for Confident Feature Selection 利用神经网络和校准措施进行自信特征选择

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-04-14 DOI: 10.1109/TETCI.2025.3535659

Hassan Gharoun;Navid Yazdanjue;Mohammad Sadegh Khorshidi;Fang Chen;Amir H. Gandomi

{"title":"Leveraging Neural Networks and Calibration Measures for Confident Feature Selection","authors":"Hassan Gharoun;Navid Yazdanjue;Mohammad Sadegh Khorshidi;Fang Chen;Amir H. Gandomi","doi":"10.1109/TETCI.2025.3535659","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3535659","url":null,"abstract":"With the surge in data generation, both vertically (i.e., volume of data) and horizontally (i.e., dimensionality) the burden of the curse of dimensionality has become increasingly palpable. Feature selection, a key facet of dimensionality reduction techniques, has advanced considerably to address this challenge. One such advancement is the Boruta feature selection algorithm, which successfully discerns meaningful features by contrasting them to their permutated counterparts known as shadow features. Building on this, this paper introduces NeuroBoruta, that extends the traditional Boruta approach by integrating neural networks and calibration metrics to improve prediction accuracy and reduce model uncertainty. By augmenting shadow features with noise and utilizing neural network-based perturbation for importance evaluation, and further incorporating calibration metrics alongside accuracy this evolved version of the Boruta method is presented. Experimental results demonstrate that NeuroBoruta significantly enhances the predictive performance and reliability of classification models across various datasets, including medical imaging and standard UCI datasets. This study underscores the importance of considering both feature relevance and model uncertainty in the feature selection process, particularly in domains requiring high accuracy and reliability.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2179-2193"},"PeriodicalIF":5.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964323","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Exploration in Deep Reinforcement Learning for Incomplete Information Competition Environments 不完全信息竞争环境下深度强化学习的改进探索

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-04-14 DOI: 10.1109/TETCI.2025.3555250

Jie Lin;Yuhao Ye;Shaobo Li;Hanlin Zhang;Peng Zhao

{"title":"Improving Exploration in Deep Reinforcement Learning for Incomplete Information Competition Environments","authors":"Jie Lin;Yuhao Ye;Shaobo Li;Hanlin Zhang;Peng Zhao","doi":"10.1109/TETCI.2025.3555250","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3555250","url":null,"abstract":"The sparse reward problem widely exists in multi-agent deep reinforcement learning, preventing agents from learning optimal actions and resulting in inefficient interactions with the environment. Many efforts have been made to design denser rewards and promote agent exploration. However, existing methods only focus on the breadth of action exploration, neglecting the rationality of action exploration in deep reinforcement learning, which leads to inefficient action exploration for agents. To address this issue, in this paper, we propose a novel curiosity-based action exploration method in incomplete information competition game environments, namely IGC, to improve both the breadth and rationality of action exploitation in multi-agent deep reinforcement learning for sparse-reward environments. Particularly, to enhance the capability of action exploration for agents, the distance reward is designed in our IGC method to increase the density of rewards in action exploration, thereby mitigating the sparse reward problem. In addition, by integrating the Intrinsic Curiosity Module (ICM) into DQN, we propose an enhanced ICM-DQN module, which enhances the breadth and rationality of subject action exploration for agents. By doing this, our IGC method can mitigate the randomness of the existing curiosity mechanism and increase the rationality of action exploration of agents, thereby enhancing the efficiency of action exploration. Finally, we evaluate the effectiveness of our IGC method on an incomplete information card game, namely Uno card game. The results demonstrate that our IGC method can achieve both better action exploration efficiency and greater winning-rate in comparison with existing methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 5","pages":"3665-3676"},"PeriodicalIF":5.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0