Efficient neuroevolution for a quadruped robot

Shengbo Xu*, Hirotaka Moriguchi, Shinichi Honiden

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


In this research, we investigate whether CoSyNE and CMA-NeuroES algorithms can efficiently optimize neural policy of a quadruped robot. Both of these algorithms are proven to optimize connection weights efficiently on Pole Balancing benchmark. Due to their good results on that benchmark, they are expected to be efficient on other control problems like gait generation. In this research we experimentally show that CMA-NeuroES have higher scalability to optimize Artificial Neural Networks for generating gaits of quadruped robots in comparison with CoSyNE. The results can be helpful for researchers and practitioners to choose the optimal Neuroevolution algorithm for generating gaits.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning - 9th International Conference, SEAL 2012, Proceedings
Number of pages10
Publication statusPublished - 2012
Externally publishedYes
Event9th International Conference on Simulated Evolution and Learning, SEAL 2012 - Hanoi, Viet Nam
Duration: 2012 Dec 162012 Dec 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7673 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th International Conference on Simulated Evolution and Learning, SEAL 2012
Country/TerritoryViet Nam


  • CMA-ES
  • CMA-NeuroES
  • CoSyNE
  • Simplex
  • evolution
  • neural network
  • neuroevolution

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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