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检索条件"主题词=Multifactorial Evolutionary Algorithm"
42 条 记 录,以下是21-30 订阅
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Multitask Augmented Random Search in deep reinforcement learning
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APPLIED SOFT COMPUTING 2024年 160卷
作者: Thanh, Le Tien Thang, Ta Bao Van Cuong, Le Binh, Huynh Thi Thanh Grad Univ Adv Studies Dept Informat SOKENDAI Tokyo Japan Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam
Reinforcement Learning (RL) has gained significant popularity in recent years for its ability to solve complex control problems. However, most existing RL algorithms are designed to train policies for each environment... 详细信息
来源: 评论
Contrastive variational auto-encoder driven convergence guidance in evolutionary multitasking
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APPLIED SOFT COMPUTING 2024年 163卷
作者: Wang, Ruilin Feng, Xiang Yu, Huiqun Shanghai Engn Res Ctr Smart Energy Shanghai 200237 Peoples R China
Knowledge transfer is at the core of the evolutionary Multitasking (EMT) problem, as it exploits the interaction of inter-task common knowledge to accelerate task convergence. However, existing research on EMT is gene... 详细信息
来源: 评论
Multi-objective evolutionary multi-tasking algorithm using cross-dimensional and prediction-based knowledge transfer
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INFORMATION SCIENCES 2022年 586卷 540-562页
作者: Chen, Qunjian Ma, Xiaoliang Yu, Yanan Sun, Yiwen Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China BGI Shenzhen Shenzhen 518083 Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen Peoples R China
Transfer learning is an important research topic in machine learning and recently has been introduced into evolutionary computation to form evolutionary multi-task optimization (EMTO). EMTO focuses on tackling multipl... 详细信息
来源: 评论
System-in-package design using multi-task memetic learning and optimization
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MEMETIC COMPUTING 2022年 第1期14卷 45-59页
作者: Dai, Weijing Wang, Zhenkun Xue, Ke Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China
System-in-Package (SiP) is an advanced packaging technology and developing rapidly in semiconductor industry. Electronic modules of this package type are individual integrated systems for specific applications. Theref... 详细信息
来源: 评论
evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions
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COGNITIVE COMPUTATION 2022年 第3期14卷 927-954页
作者: Osaba, Eneko Del Ser, Javier Martinez, Aritz D. Hussain, Amir Basque Res & Technol Alliance BRTA TECNALIA Ed 700 Derio 48160 Spain Univ Basque Country UPV EHU Bilbao 48013 Spain Edinburgh Napier Univ Edinburgh Midlothian Scotland
In this work, we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamicall... 详细信息
来源: 评论
Two levels approach based on multifactorial optimization to solve the clustered shortest path tree problem
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evolutionary INTELLIGENCE 2022年 第1期15卷 185-213页
作者: Huynh Thi Thanh, Binh Pham Dinh, Thanh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Taybac Univ Fac Nat Sci & Technol Son La Vietnam
The Clustered Shortest-Path Tree Problem (CluSPT) has a great meaning in theoretical research as well as a wide range of applications in everyday life, especially in the field of network optimization. Being able to so... 详细信息
来源: 评论
A hybrid and adaptive evolutionary approach for multitask optimization of post-disaster traveling salesman and repairman problems
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COMPUTERS & OPERATIONS RESEARCH 2024年 167卷
作者: Ban, Ha-Bang Binh, Huynh Thi Thanh Do, Tuan Anh Tran, Cong Dao Nguyen, Su Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam RMIT Univ Melbourne Australia FPT Software AI Ctr Hanoi Vietnam
Traveling Salesman Problem (TSP) and Traveling Repairman Problem (TRP) have been studied due to their real -world applications. While the TSP minimizes the total time to travel to all customers, the TRP minimizes the ... 详细信息
来源: 评论
Node depth Representation-based evolutionary Multitasking Optimization for Maximizing the Network Lifetime of Wireless Sensor Networks
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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷
作者: Dao, Tran Cong Tam, Nguyen Thi Binh, Huynh Thi Thanh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam VNU Univ Sci Hanoi Vietnam FPT Software AI Ctr Hanoi Vietnam
Wireless Sensor Networks (WSNs) face challenges related to limited energy resources in sensor nodes, making network lifetime and energy consumption critical considerations. In this paper, we introduce a novel approach... 详细信息
来源: 评论
evolutionary Constrained Multi-task Optimization: Benchmark Problems and Preliminary Results  22
Evolutionary Constrained Multi-task Optimization: Benchmark ...
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Genetic and evolutionary Computation Conference (GECCO)
作者: Li, Yanchi Gong, Wenyin Li, Shuijia Univ Geosci Sch Comp Sci China Wuhan Peoples R China
Multi-task optimization (MTO) aims to solve multiple tasks simultaneously. However, multi-task evolutionary algorithms (MTEAs) hardly consider the problem with constraints, while most optimization problems, in reality... 详细信息
来源: 评论
Enhancing evolutionary multitasking optimization by leveraging inter-task knowledge transfers and improved evolutionary operators
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KNOWLEDGE-BASED SYSTEMS 2023年 259卷
作者: Ma, Xiaoliang Xu, Mingyu Yu, Yanan Liu, Hongjie Wang, Yan Wang, Lei Qi, Yutao Xiong, Jian Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT Shenzhen 518055 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu 610074 Peoples R China
It is inefficient and time-consuming to begin the search from scratch for each optimization task. evolutionary multitasking optimization (EMTO) handles multiple tasks simultaneously, aiming at im-proving the solving q... 详细信息
来源: 评论