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- W2475726324 abstract "A new indirect scheme for encoding neural network connection weights as sets of wavelet-domain coefficients is proposed in this paper. It exploits spatial regularities in the weight-space to reduce the gene-space dimension by considering the low-frequency wavelet coefficients only. The wavelet-based encoding builds on top of a frequency-domain encoding, but unlike when using a Fourier-type transform, it offers gene locality while preserving continuity of the genotype-phenotype mapping. We argue that this added property allows for more efficient evolutionary search and demonstrate this on the octopus-arm control task, where superior solutions were found in fewer generations. The scalability of the wavelet-based encoding is shown by evolving networks with many parameters to control game-playing agents in the Arcade Learning Environment." @default.
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- W2475726324 date "2016-07-20" @default.
- W2475726324 modified "2023-10-18" @default.
- W2475726324 title "A Wavelet-based Encoding for Neuroevolution" @default.
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- W2475726324 doi "https://doi.org/10.1145/2908812.2908905" @default.
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