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- W2897801744 abstract "Genetic Programming (GP) is an important technique in evolutionary computing. There has been extensive research and great achievement in GP and its variants. Grammar-based genetic programming (GGP) is one of the most promising ones. We propose here a hybrid approach of GGP with Evolution Strategies (ES). GGP is used to evolve the structure of the models while ES searches for the numerical coefficients in order to improve the overall performance when solving symbolic regression problems. Computational experiments conducted on a set of test-cases reveal that the proposed hybrid approach achieved a good performance when compared to other methods from the literature." @default.
- W2897801744 created "2018-10-26" @default.
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- W2897801744 date "2018-07-01" @default.
- W2897801744 modified "2023-09-27" @default.
- W2897801744 title "A Hybrid Grammar-Based Genetic Programming for Symbolic Regression Problems" @default.
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- W2897801744 doi "https://doi.org/10.1109/cec.2018.8477826" @default.
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