A Wavelet-based Encoding for Neuroevolution

S. van Steenkiste*, J. Koutnik, K. Driessens, J. Schmidhuber

*Corresponding author for this work

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
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)9781450342063
Publication statusPublished - 2016
EventGenetic and Evolutionary Computation Conference (GECCO) - Denver, United States
Duration: 20 Jul 201624 Jul 2016


ConferenceGenetic and Evolutionary Computation Conference (GECCO)
Abbreviated titleGECCO 2016
Country/TerritoryUnited States
Internet address


  • Neuroevolution
  • indirect encoding
  • wavelets
  • gene-locality

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