Evolving stable behavior in a spino-neuromuscular system model

This paper demonstrates the effectiveness of genetic algorithms in training stable behavior in a model of the spinoneuromuscular system (SNMS). In particular, we test the stability of trained instances of the model with respect to unfamiliar control signals and untrained forearm weights. The results show that small changes to the input frequency and forearm weight result in small changes in velocity, demonstrating that the system can reasonably accommodate unfamiliar circumstances. This type of stability is a critical feature for virtually any type of control system. Categories and Subject Descriptors I.2 [Computing Methodologies]: Artificial Intelligence General Terms Algorithms Keywords Neural Networks, Genetic Algorithms, Robotics

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Stanley Phillips Gotshall, Terry Soule
• Keywords: Forearm Weight, GECCO 2008, Genetic Algorithms, Optimization, Untrained Forearm Weights