# A Frequency-Domain Encoding for Neuroevolution

@article{Koutnk2012AFE, title={A Frequency-Domain Encoding for Neuroevolution}, author={Jan Koutn{\'i}k and J{\"u}rgen Schmidhuber and Faustino J. Gomez}, journal={ArXiv}, year={2012}, volume={abs/1212.6521} }

Neuroevolution has yet to scale up to complex reinforcement learning tasks that require large networks. Networks with many inputs (e.g. raw video) imply a very high dimensional search space if encoded directly. Indirect methods use a more compact genotype representation that is transformed into networks of potentially arbitrary size. In this paper, we present an indirect method where networks are encoded by a set of Fourier coefficients which are transformed into network weight matrices via an…

## 4 Citations

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### COMPRESSED WEIGHT SPACE

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The goal is to open a discussion on this topic, starting with recurrent neural networks for character-level language modelling whose weight matrices are encoded by the discrete cosine transform, and using a recurrent neural network to parameterise the compressed weights.

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