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Asynchronous Parallel Evolutionary Algorithms for Constrained Optimizations 认领 引用
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作者 Kang Li-shan Liu Pu +2 位作者 Kang Zhuo Li Yan Chen Yu-ping 《Wuhan University Journal of Natural Sciences》 EI CAS 2000年第4期406-412,共7页
Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the pop... Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the higher accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous parallel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm. 展开更多
关键词 asynchronous parallel evolutionary algorithm function optimization
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Financial Data Modeling by Using Asynchronous Parallel Evolutionary Algorithms 认领 引用
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作者 Wang Chun, Li Qiao-yunSchool of Business, Huazhong University of Science and Technology , Wuhan 4300741 Hubei ChinaNetwork and Software Technology Center of America, Sony Corporation San Jose, CA, USA 《Wuhan University Journal of Natural Sciences》 EI CAS 2003年第S1期239-242,共4页
In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A n... In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example of Nasdaq index analysis is used to demonstrate the potential of APHEMA. The results show that the dynamic models automatically discovered in dynamic data by computer can be used to predict the financial trends. 展开更多
关键词 financial data mining asynchronous parallel algorithm knowledge discovery evolutionary modeling
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