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 genspace 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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Genetic and Evolutionary Computation Conference |
| Pages | 517-524 |
| Publication status | Published - 2016 |
Cite this
van Steenkiste, S., Koutnik, J., Schmidhuber, J., & Driessens, K. (2016). A Wavelet-based Encoding for Neuroevolution. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 517-524)