Name
Abstract | ||
|---|---|---|
This paper presents an investigation into exploiting the population-based nature of Learning Classifier Systems for their use within highly-parallel systems. In particular, the use of simple accuracy-based Learning Classifier Systems within the ensemble machine approach is examined. Results indicate that inclusion of a rule migration mechanism inspired by Parallel Genetic Algorithms is an effective way to improve learning speed. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/CEC.2005.1554739 | 2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS |
Keywords | Field | DocType |
parallel systems,learning classifier system,genetic algorithms,learning artificial intelligence,parallel algorithms | Multi-task learning,Margin (machine learning),Stability (learning theory),Semi-supervised learning,Active learning (machine learning),Computer science,Artificial intelligence,Margin classifier,Ensemble learning,Machine learning,Learning classifier system | Conference |
Citations | PageRank | References |
9 | 0.54 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Larry Bull | 1 | 38 | 3.96 |
| Matthew Studley | 2 | 46 | 7.40 |
| Anthony Bagnall | 3 | 970 | 53.36 |
| Ian Whittley | 4 | 11 | 1.28 |