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Generalization in the xcs classifier system (1998).
| Content Provider | CiteSeerX |
|---|---|
| Author | Wilson, S. W. |
| Abstract | This paper studies two changes to XCS, a classifier system in which fitness is based on prediction accuracy and the genetic algorithm takes place in environmental niches. The changes were aimed at increasing XCS's tendency to evolve accurate, maximally general classifiers and were tested on previously employed "woods" and multiplexer tasks. Together the changes bring XCS close to evolving populations whose high-fitness classifiers form a near-minimal, accurate, maximally general cover of the input and action product space. In addition, results on the multiplexer, a difficult categorization task, suggest that XCS's learning complexity is polynomial in the input length and thus may avoid the "curse of dimensionality", a notorious barrier to scale-up. A comparison between XCS and genetic programming in solving the 6multiplexer suggests that XCS's learning rate is about three orders of magnitude faster in terms of the number of input instances processed. |
| File Format | |
| Publisher Date | 1998-01-01 |
| Access Restriction | Open |
| Subject Keyword | Xc Classifier System Environmental Niche Prediction Accuracy Genetic Programming Multiplexer Task Input Length Classifier System General Cover General Classifier Notorious Barrier Input Instance Genetic Algorithm Learning Complexity Paper Study Difficult Categorization Task Action Product Space High-fitness Classifier |
| Content Type | Text |
| Resource Type | Article |
