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- W2034192072 abstract "We present a new distributed genetic algorithm that can be used to extract useful information from distributed, large data over the network. The main idea of the proposed algorithms is to determine how many and which individuals move between subpopulations at each site adaptively. In addition, we present a method to help individuals from other subpopulations not be weeded out but adapt to the new subpopulation. We used six data sets from UCI Machine Learning Repository to compare the performance of our approach with that of the single, centralized genetic algorithm. As a result, the proposed algorithm produced better performance than the single genetic algorithm in terms of the classification accuracy with the feature subsets." @default.
- W2034192072 created "2016-06-24" @default.
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- W2034192072 date "2010-04-30" @default.
- W2034192072 modified "2023-09-25" @default.
- W2034192072 title "Distributed Genetic Algorithm using Automatic Migration Control" @default.
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- W2034192072 doi "https://doi.org/10.3745/kipstb.2010.17b.2.157" @default.
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