首页|Reports from Jiangsu University Describe Recent Advances in Intelligent Systems (Improving Two-layer Encoding of Evolutionary Algorithms for Sparse Large-scale Multiobjective Optimization Problems)
Reports from Jiangsu University Describe Recent Advances in Intelligent Systems (Improving Two-layer Encoding of Evolutionary Algorithms for Sparse Large-scale Multiobjective Optimization Problems)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Machine Learning - Intellige nt Systems are discussed in a new report. According to news reporting from Jiang su, People’s Republic of China, by NewsRx journalists, research stated, “Sparse large-scale multiobjective problems (LSMOPs) are characterized as an NP-hard iss ue that undergoes a significant presence of zero-valued variables in Pareto opti mal solutions. In solving sparse LSMOPs, recent studies typically employ a speci alized two-layer encoding, where the low-level layer undertakes the optimization of zero variables and the high-level layer is in charge of non-zero variables.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of China (NSFC), Natural Science Research Project of Anhui Educational Committee.
JiangsuPeople's Republic of ChinaAsi aIntelligent SystemsMachine LearningAlgorithmsEvolutionary AlgorithmMa thematicsJiangsu University
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