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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Zhang, Tiancong Chu, Shu-Chuan Weng, Shaowei Dao, Thi-Kien Nguyen, Trong-The School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

Genetic algorithms (GAs) are a powerful class of optimization techniques inspired by the principles of natural selection and genetics. One of the theoretical cornerstones of GAs is schema theory, which provides a framework for understanding how building blocks of solutions, known as schemas, are propagated and combined through generations. This review paper aims to provide a comprehensive theoretical analysis of schema formation in genetic algorithms. It delves into the foundational principles of schema theory, including the schema theorem and its implications for the efficiency and behavior of GAs. The paper also explores advanced topics such as schema disruption mechanisms, the impact of multimodal and epistatic fitness landscapes, and the integration of schema theory with hybrid approaches. Additionally, practical implications and real-world applications of schema theory are discussed through case studies and best practices. By elucidating the mechanisms of schema formation and their influence on genetic algorithm performance, this review seeks to offer valuable insights and directions for future research. Ultimately, this theoretical exploration aims to bridge the gap between abstract concepts and practical application, contributing to the advancement of genetic algorithms as robust optimization tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Evolving Effective Drug Therapies with Multi-objective Genetic Algorithm 16th

Evolving Effective Drug Therapies with Multi-objective Genet...

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Zhang, Tiancong Weng, Shaowei Chu, Shu-Chuan Nguyen, Trong-The Dao, Thi-Kien School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

The development of effective drug therapies is a complex and multifaceted problem involving various biological, chemical, and computational challenges. Traditional drug development methods are often time-consuming and costly, necessitating innovative approaches. Multi-objective genetic algorithms (MOGAs) have emerged as a powerful tool for optimizing drug design by simultaneously considering multiple conflicting objectives, such as efficacy, safety, and cost. This paper reviews the application of MOGAs in drug discovery, highlighting their principles, advantages, and case studies, and discussing future directions in the field. Enhancing MOGAs in drug discovery are also outlined, emphasizing the integration with machine learning, advancements in computational infrastructure, and the potential for personalized medicine. The paper concludes by affirming the significant promise of MOGAs in accelerating the development of safe and effective drug therapies, thereby addressing critical needs in modern pharmacology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drug discovery

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of Computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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On the Performance of Active RIS-Assisted Mixed RF-THz Relaying Systems

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IEEE Internet of Things Journal 2025年第10期12卷 14938-14951页

作者： Yin, Yiyang Yang, Liang Li, Xingwang Liu, Hongwu Guo, Kefeng Li, Yingsong Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Jishou University School of Communication and Electronic Engineering Jishou416000 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Space Engineering University School of Space Information Beijing101407 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

We investigate the performance of an active reconfigurable intelligent surface (RIS)-assisted mixed radio frequency (RF)-terahertz (THz) relaying system, where the RF signal reaches the relay through the active RIS and is then transmitted to the user via the THz channel. Under this scenario, we analyze the system performance with the relay employing amplify-and-forward (AF) and decode-and-forward (DF) protocols. More specifically, we derive the exact expressions for the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) for both relaying protocols. Based on this, we obtain the exact expressions for the outage probability (OP), average bit error rate (ABER), and average channel capacity (ACC). Furthermore, to gain deeper insights, we derive the asymptotic expressions at high SNRs and obtain diversity order (DO) of the system. Moreover, we extend the analysis to the variable gain relaying scheme. The findings reveal that the DOs for both relaying protocols are determined by the THz channel parameters, and the DO under the AF relaying protocol is twice that of the DF relaying protocol. Finally, we validate that active RIS (A-RIS) can more effectively assist the performance of the mixed RF-THz relaying system compared to passive RIS. © 2014 IEEE.

关键词： Distribution functions

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Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

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IEEE Transactions on Multimedia 2025年

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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High-order diversity feature learning for pedestrian attribute recognition

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Neural Networks 2025年 188卷 107463页

作者： Wu, Junyi Huang, Yan Gao, Min Niu, Yuzhen Chen, Yuzhong Wu, Qiang Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou China Australian Artificial Intelligence Institute University of Technology Sydney NSW Australia Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fujian Fuzhou China School of Electrical and Data Engineering University of Technology Sydney NSW Australia

Pedestrian attribute recognition (PAR) involves accurately identifying multiple attributes present in pedestrian images. There are two main approaches for PAR: part-based method and attention-based method. The former relies on existing segmentation or region detection methods to localize body parts and learn corresponding attribute-specific feature from the corresponding regions, where the performance heavily depends on the accuracy of body region localization. The latter adopts the embedded attention modules or transformer attention to exploit detailed feature. However, it can focus on certain body regions but often provide coarse attention, failing to capture fine-grained details, the learned feature may also be interfered with by irrelevant information. Meanwhile, these methods overlook the global contextual information. This work argues for replacing coarse attention with detailed attention and integrating it with global contextual feature from ViT to jointly represent attribute-specific regions. To tackle this issue, we propose a High-order Diversity Feature Learning (HDFL) method for PAR based on ViT. We utilize a polynomial predictor to design an Attribute-specific Detailed Feature Exploration (ADFE) module, which can construct the high-order statistics and gain more fine-grained feature. Our ADFE module is a parameter-friendly method that provides flexibility in deciding its utilization during the inference phase. A Soft-redundancy Perception Loss (SPLoss) is proposed to adaptively measure the redundancy between feature of different orders, which can promote diverse characterization of features. Experiments on several PAR datasets show that our method achieves a new state-of-the-art (SOTA) performance. On the most challenging PA100K dataset, our method outperforms previous SOTA by 1.69% and achieves the highest mA of 84.92%. © 2025 The Authors

关键词： Contrastive Learning

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Relaxing Distillation Constraints for Improved New Class Learning in Continual Semantic Segmentation

Relaxing Distillation Constraints for Improved New Class Lea...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zheng Ti Xuze Hao Renhai Chen College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Continual Semantic Segmentation (CSS) aims to continuously learn new classes while mitigating catastrophic forgetting. Existing CSS methods primarily address this challenge through knowledge distillation. While they focus on maintaining stability for old classes, this emphasis often restricts plasticity in learning new ones. In this work, we propose two plug-and-play methods to better balance stability and plasticity. First, for feature map distillation, we replace pixel-level constraints with statistical information extraction within a window, improving the learning of new classes while preserving structural integrity to overcome forgetting. Second, we use relative ranks of predictions to replace exact probabilistic values to loosen the constraints in the logit distribution of old classes. Extensive experiments demonstrate that our methods outperform state-of-the-art methods when integrating into several baselines across various CSS scenarios.

关键词： Semantic segmentation Semantics Signal processing Feature extraction Probabilistic logic Information retrieval Acoustics data mining Speech processing

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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

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IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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CrossSim: Towards Cross-System Trajectory Similarity Computation via Representation Learning

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IEEE Internet of Things Journal 2025年

作者： Cao, Zijian Zhao, Dong Dong, Xiyuan Wang, Qiyue Yuan, Haitao Gao, Yujia Ma, Huadong Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Computer Science Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Zhongguancun Laboratory Beijing100094 China

Trajectory similarity computation is essential for various downstream applications, such as anomaly route detection, order matching, and digital contact tracing. However, its effectiveness is confined within a single system due to privacy concerns associated with sharing raw trajectories across different systems. In this paper, we propose CrossSim, a novel framework designed to efficiently retrieve similar trajectories across all systems while preserving individual privacy. Our framework comprises three main components: i) a Trajectory Encoding Model that transforms trajectories into high-quality representations, where similarity relationships are reflected by their distances;ii) a two-stage optimization mechanism, including a Contrastive Similarity Learning stage and a Federated Similarity Learning stage, that alleviates the impact of heterogeneous similarity relationships across different systems on model training without aggregating raw trajectories;iii) a Similar Trajectory Retrieval procedure that obtains top-k similar trajectories from all systems without sharing raw trajectories. We conduct comprehensive experiments on three real-world datasets to evaluate the effectiveness of our proposed framework. The evaluation results demonstrate that CrossSim outperforms all existing schemees. © 2014 IEEE.

关键词： Federated learning

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LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena

arXiv

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arXiv 2025年

作者： Ma, Tianmi Du, Jiawei Huang, Wenxin Wang, Wenjie Xie, Liang Zhong, Xian Zhou, Joey Tianyi Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computing National University of Singapore Singapore School of Science Wuhan University of Technology China

Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks. However, their ability to generalize to dynamic, unseen tasks, particularly in numerical reasoning, remains a challenge. Existing benchmarks mainly evaluate LLMs on problems with predefined optimal solutions, which may not align with real-world scenarios where clear answers are absent. To bridge this gap, we design the Agent Trading Arena, a virtual numerical game simulating complex economic systems through zero-sum games, where agents invest in stock portfolios. Our experiments reveal that LLMs, including GPT-4o, struggle with algebraic reasoning when dealing with plain-text stock data, often focusing on local details rather than global trends. In contrast, LLMs perform significantly better with geometric reasoning when presented with visual data, such as scatter plots or K-line charts, suggesting that visual representations enhance numerical reasoning. This capability is further improved by incorporating the reflection module, which aids in the analysis and interpretation of complex data. We validate our findings on NASDAQ STOCK dataset, where LLMs demonstrate stronger reasoning with visual data compared to text. Our code and data are publicly available at https://***/wekjsdvnm/***. © 2025, CC BY.

关键词： Natural language processing systems

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