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Published on Oct 20, 2012
Neural networks evolved to produce gaits for legged robots. The use of the HyperNEAT generative encoding produces geometric patterns (regularities) in the neural wiring of the evolved brains, which improves performance by producing coordinated, regular leg movements.
Evolving artificial neural networks (ANNs) and gaits for robots are difficult, time-consuming tasks for engineers, making them suitable for evolutionary algorithms (aka genetic algorithms). Generative encodings (aka indirect and developmental encodings) perform better than direct encodings by producing neural regularities that result in behavioral regularities.
References: • Clune J, Stanley KO, Pennock RT, Ofria C (2011) On the performance of indirect encoding across the continuum of regularity. IEEE Transactions on Evolutionary Computation. 15(3): 346-367. • Clune J, Beckmann BE, Ofria C, and Pennock RT (2009) Evolving coordinated quadruped gaits with the HyperNEAT generative encoding. Proceedings of the IEEE Congress on Evolutionary Computing. 2762-2771. • Yosinski J, Clune J, Hidalgo D, Nguyen S, Cristobal Zagal J, Lipson H (2011) Evolving robot gaits in hardware: the HyperNEAT generative encoding vs. parameter optimization. Proceedings of the European Conference on Artificial Life. 890-897. • Lee S, Yosinski J, Glette K, Lipson H, Clune J (2013) Evolving robot gaits for physical robots with generative encodings: the benefits of simulation. In preparation.