標題: Parameter adaptation within co-adaptive learning classifier systems
作者: Huang, CY
Sun, CT
資訊工程學系
Department of Computer Science
公開日期: 2004
摘要: The authors propose a co-adaptive approach to controlling parameters for coevolution-based learning classifier systems. 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 learning classifier systems can operate in parallel to simultaneously solve target and parametersetting 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. Our experimental results show that the proposed system outperforms comparable models regardless of a problem's stationary/non-stationary status.
URI: http://hdl.handle.net/11536/27201
ISBN: 3-540-22343-6
ISSN: 0302-9743
期刊: GENETIC AND EVOLUTIONARY COMPUTATION GECCO 2004 , PT 2, PROCEEDINGS
Volume: 3103
起始頁: 774
結束頁: 784
顯示於類別:會議論文