A Wavelet-based Encoding for Neuroevolution

Sjoerd van Steenkiste, Jan Koutnik, Jurgen Schmidhuber, Kurt Driessens

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


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 languageEnglish
Title of host publicationProceedings of the Genetic and Evolutionary Computation Conference
Publication statusPublished - 2016

Cite this