We propose a co-adaptive approach to control coevolution-based eXtended Classifier System (XCS) parameters. By taking advantage of the on-line incremental learning capability of such systems, solutions can be produced that completely cover a target problem. The system combines the advantages of both adaptive and self-adaptive parameter-control approaches. Using a coevolution model means that two XCS can operate in parallel to simultaneously solve target and parameter-setting problems. Furthermore, the approach needs very little time to become efficient in terms of latent learning, since it only requires small amounts of information on performance metrics during early run-time stages. Test results indicate that our proposed system outperforms comparable models regardless of the target problem's stationary/non-stationary status.
|Number of pages||7|
|Journal||WSEAS Transactions on Systems|
|State||Published - 1 Feb 2006|
- Parameter adaptation
- Parameter setting problem