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检索条件"主题词=multi-objective evolutionary algorithm"
545 条 记 录,以下是161-170 订阅
排序:
A pattern-driven solution for designing multi-objective evolutionary algorithms
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NATURAL COMPUTING 2020年 第3期19卷 481-494页
作者: Guizzo, Giovani Vergilio, Silvia R. DInf Fed Univ Parana CP 19081 BR-19031970 Curitiba PR Brazil
multi-objective evolutionary algorithms (MOEAs) have been widely studied in the literature, which led to the development of several frameworks and techniques to implement them. Consequently, the reusability, scalabili... 详细信息
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A fuzzy based approach for fitness approximation in multi-objective evolutionary algorithms
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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015年 第5期29卷 2111-2131页
作者: Pourbahman, Zahra Hamzeh, Ali Shiraz Univ Dept Elect & Comp Engn Shiraz Iran
evolutionary algorithm provides a framework that is largely applicable to particular problems including multiobjective optimization problems, basically for the ease of their implementation and their capability to perf... 详细信息
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Data Structures in multi-objective evolutionary algorithms
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Journal of Computer Science & Technology 2012年 第6期27卷 1197-1210页
作者: Najwa Altwaijry Mohamed El Bachir Menai Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178 Riyadh 11453Saudi Arabia
Data structures used for an algorithm can have a great impact on its performance, particularly for the solution of large and complex problems, such as multi-objective optimization problems (MOPs). multi-objective ev... 详细信息
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A new stopping criterion for multi-objective evolutionary algorithms: application in the calibration of a hydrologic model
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COMPUTATIONAL GEOSCIENCES 2019年 第6期23卷 1219-1235页
作者: Ticona Gutierrez, Juan Carlos Adamatti, Daniela Santini Bravo, Juan Martin Univ Fed Rio Grande do Sul Inst Pesquisas Hidraul Porto Alegre RS Brazil
multi-objective genetic algorithms have been successfully applied in a wide variety of problems. Although widely used, there are few theoretical guidelines for determining when to stop the search. Many users commonly ... 详细信息
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A co-evolutionary algorithm based on sparsity clustering for sparse large-scale multi-objective optimization
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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 第PartB期133卷
作者: Zhang, Yajie Wu, Chengming Tian, Ye Zhang, Xingyi Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China
Sparse large-scale multi -objective optimization problems (LSMOPs), which are characterized by high dimensional search space and sparse Pareto optimal solutions, have a widespread existence in academic research and pr... 详细信息
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Do Search and Selection Operators Play Important Roles in multi-objective evolutionary algorithms:A Case Study
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Wuhan University Journal of Natural Sciences 2003年 第S1期8卷 195-201页
作者: Yan Zhen-yu, Kang Li-shan, Lin Guang-ming ,He MeiState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, ChinaSchool of Computer Science, UC, UNSW Australian Defence Force Academy, Northcott Drive, Canberra, ACT 2600 AustraliaCapital Bridge Securities Co. ,Ltd, Floor 42, Jinmao Tower, Shanghai 200030, China Wuhan University State Key Laboratory of Software Engineering Wuhan Hubei China (GRID:grid.49470.3e) (ISNI:***) UC UNSW Australian Defence Force Academy School of Computer Science Canberra Australia (GRID:grid.97008.36) (ISNI:***) Capital Bridge Securities Co. Ltd Shanghai China (GRID:grid.97008.36)
multi-objective evolutionary algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search an... 详细信息
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A computation offloading algorithm based on multi-objective evolutionary optimization in mobile edge computing
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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 第1期121卷
作者: Chai, Zheng-Yi Liu, Xu Li, Ya-Lun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tiangong Univ Sch Software Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China
For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, term... 详细信息
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Optimization of a Demand Responsive Transport Service Using multi-objective evolutionary algorithms  19
Optimization of a Demand Responsive Transport Service Using ...
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Genetic and evolutionary Computation Conference (GECCO)
作者: Viana, Renan J. S. Santos, Andre G. Martins, Flavio V. C. Wanner, Elizabeth F. CEFET MG Programa Posgrad Modelagem Matemat & Computac Belo Horizonte MG Brazil Univ Fed Vicosa Vicosa MG Brazil Ctr Fed Educ Tecnol Minas Gerais Belo Horizonte MG Brazil Aston Univ Birmingham W Midlands England
This paper addresses the problem of optimizing a Demand Responsive Transport (DRT) service. A DRT is a flexible transportation service that provides on-demand transport for users who formulate requests specifying desi... 详细信息
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BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING multi-objective evolutionary algorithmS  14
BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING MULTI-OB...
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International Conference on Machine Learning and Cybernetics (ICMLC)
作者: Golchin, Maryam Davarpanah, Seyed Hashem Liew, Alan Wee-Chung Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia
Clustering is an unsupervised learning technique that groups data into clusters using the entire conditions. However, sometimes, data is similar only under a subsetof conditions. Biclustering allows clustering of rows... 详细信息
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What Performance Indicators to Use for Self-Adaptation in multi-objective evolutionary algorithms
What Performance Indicators to Use for Self-Adaptation in Mu...
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Genetic and evolutionary Computation Conference (GECCO)
作者: Ye, Furong Neumann, Frank de Nobel, Jacob Neumann, Aneta Back, Thomas Chinese Acad Sci ISCAS Beijing Peoples R China Univ Adelaide Adelaide SA Australia Leiden Univ LIACS Leiden Netherlands
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms. While empirical and theoretical studies have shed light on the behavior of algorithms for single-objective optimizati... 详细信息
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