Particle swarm optimization for unsupervised robotic learning

We explore using particle swarm optimization on problems with noisy performance evaluation, focusing on unsupervised robotic learning. We adapt a technique of overcoming noise used in genetic algorithms for use with particle swarm optimization, and evaluate the performance of both the original algorithmand the noise-resistantmethod for several numerical problems with added noise, as well as unsupervised learning of obstacle avoidance using one or more robots.


Published in:
Swarm Intelligence Symposium, 92-99
Presented at:
Swarm Intelligence Symposium, California, US, 8-10 June, 2005
Year:
2005
Keywords:
Laboratories:




 Record created 2005-05-25, last modified 2018-06-20

n/a:
Download fulltextPDF
External link:
Download fulltextURL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)