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
Original language | English |
---|---|
Pages | 612-617 |
Number of pages | 6 |
DOIs | |
Publication status | Published - Sep 2005 |
Event | 2005 Congress on Evolutionary Computation - Edinburgh, Scotland Duration: 5 Sep 2005 → … |
Conference
Conference | 2005 Congress on Evolutionary Computation |
---|---|
City | Edinburgh, Scotland |
Period | 5/09/05 → … |