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.
|Original language||English (US)|
|Title of host publication||GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference|
|Publisher||Association for Computing Machinery, Incacmhelp@acm.org|
|Number of pages||8|
|State||Published - Jul 20 2016|