Neural Nets Trained by Genetic Algorithms for Collision Avoidance
Applied Intelligence
Learning Temporally Encoded Patterns in Networks of SpikingNeurons
Neural Processing Letters
Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots
ER '01 Proceedings of the International Symposium on Evolutionary Robotics From Intelligent Robotics to Artificial Life
Controlling the Speed of Synfire Chains
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Breeding swarms: a GA/PSO hybrid
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems that processes sensory information in a way similar to humans. Artificial neural networks have been an increasingly popular option for thisbecause they consist of processing units that approximate thesynaptic activity of biological signal processing units, i.e. neurons. In this paper we train a nonlinear recurrent spino-neuromuscular system model(SNMS) comparing the performance of genetic algorithms (GA)s, particle swarm optimizers (PSO)s, and GA/PSO hybrids. This model includes several key features of the SNMS that have previously been modeled individually but have not been combined into a single model as is done here. The results show that each algorithm produces fit solutions and generates fundamental biological behaviors that are not directly trained for such as tonic tension behaviors and tricepsactivation patterns.