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GECCO
2009
Springer

Evolving symmetric and modular neural networks for distributed control

8 years 9 months ago
Evolving symmetric and modular neural networks for distributed control
Problems such as the design of distributed controllers are characterized by modularity and symmetry. However, the symmetries useful for solving them are often difficult to determine analytically. This paper presents a nature-inspired approach called Evolution of Network Symmetry and mOdularity (ENSO) to solve such problems. It abstracts properties of generative and developmental systems, and utilizes group theory to represent symmetry and search for it systematically, making it more evolvable than randomly mutating symmetry. This approach is evaluated by evolving controllers for a quadruped robot in physically realistic simulations. On flat ground, the resulting controllers are as effective as those having hand-designed symmetries. However, they are significantly faster when evolved on inclined ground, where the appropriate symmetries are difficult to determine manually. The group-theoretic symmetry mutations of ENSO were also significantly more effective at evolving such control...
Vinod K. Valsalam, Risto Miikkulainen
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where GECCO
Authors Vinod K. Valsalam, Risto Miikkulainen
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