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- W2090834033 abstract "Evolutionary algorithms (EAs) are increasingly being applied to large-scale problems. MapReduce is a powerful abstraction proposed by Google for making scalable and fault tolerant applications. However, how to design high performance parallel EA based on MapReduce (MR-PEA) is still an open problem. In this paper, a parallel evolutionary algorithm model based on MapReduce by improving traditional parallel evolutionary algorithms model is proposed. The MR-PEA model is fit for large populations and datasets, has the characteristic of high scalable and efficiency. In order to justify the effectiveness of the MR-PEA model, we proposed a parallel gene expression programming based on MapReduce (MR-GEP) used to solve symbolic regression." @default.
- W2090834033 created "2016-06-24" @default.
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- W2090834033 date "2013-01-01" @default.
- W2090834033 modified "2023-10-16" @default.
- W2090834033 title "High performance parallel evolutionary algorithm model based on MapReduce framework" @default.
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- W2090834033 doi "https://doi.org/10.1504/ijcat.2013.052807" @default.
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