Paper Info
Title
"Michigan" and "Pittsburgh" Fuzzy Classifier Systems for Learning Mobile Robot Control Rules: An Experimental Comparison
Abstract
We extend our previous work on the artificial evolution of Fuzzy Classifier Systems as reactive controllers for mobile robots, to encompass more versatile genotypic representations and more powerful genetic operators. The results are an improvement on our earlier work; in general, better controllers are evolved in fewer generations. However, the more global evolutionary characteristics of the Pittsburgh approach still bias the overall results heavily in its favour. A major weakness in both approaches is the lack of robustness in retaining crucial, but seldom-active rules in the evolutionary population.
Year
Venue
Keywords
2001
FLAIRS Conference
fuzzy classifier systems,experimental comparison,learning mobile robot control,genetic operator,artificial evolution,mobile robot
Field
DocType
ISBN
Mobile robot control,Population,Evolutionary algorithm,Computer science,Robustness (computer science),Artificial intelligence,Operator (computer programming),Fuzzy classifier,Mobile robot,Machine learning
Conference
1-57735-133-9
Citations 
PageRank 
References 
1
0.35
10
Authors
2
Name
Order
Citations
PageRank
Anthony G. Pipe125539.08
Brian Carse225926.31