Inproceedings3093

XCS Revisited: A Novel Discovery Component for the eXtended Classifier System

Nugroho Fredivianus, Holger Prothmann, Hartmut Schmeck

Published: 2010

Buchtitel: Eighth International Conference on Simulated Evolution And Learning (SEAL-2010)
Verlag: Springer

Referierte Veröffentlichung
Note: Accepted for publication

BibTeX

Kurzfassung
The eXtended Classifier System (XCS) is a rule-based evolutionary on-line learning system. Originally proposed by Wilson, XCS combines techniques from reinforcement learning and evolutionary optimization to learn a population of maximally general, but accurate condition-action rules. This paper focuses on the discovery component of XCS that is responsible for the creation and deletion of rules. A novel rule combining mechanism is proposed that infers maximally general rules from the existing population. Rule combining is evaluated for single- and multi-step learning problems using the well-known multiplexer, Woods, and Maze environments. Results indicate that the novel mechanism allows for faster learning rates and a reduced population size compared to the original XCS implementation.

Projekt

OCCS (Phase III)

Forschungsgruppe

Effiziente Algorithmen

Forschungsgebiet

Organic Computing, Maschinelles Lernen