Learning classifier systems: New models, successful applications

Information Processing Letters

Volumn 82, Issue 1, 2002, Pages 23-30

  Holmes, John H ^a   Lanzi, Pier Luca ^b   Stolzmann, Wolfgang ^c   Wilson, Stewart W ^d,e  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b POLITECNICO DI MILANO   (Italy)

^c DAIMLER AG   (Germany)

^d University of Illinois at Urbana Champaign   (United States)

^e Prediction Dynamics   (United States)

Author keywords

Classifier systems; Complexity; Data mining; Generalization; Internal models; Robotics

Indexed keywords

DATA MINING; GENETIC ALGORITHMS; ROBOTICS; STATISTICS;

LEARNING CLASSIFIER SYSTEMS;

LEARNING SYSTEMS;

EID: 0037089741     PISSN: 00200190     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0190(01)00283-6     Document Type: Article

References (34)

1. Learning classifier systems: From foundations to applications
2. Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems
3. What is a learning classifier system?
4. Classifier fitness based on accuracy
5. Lookahead planning and latent learning in a classifier system
6. An introduction to anticipatory classifier systems
7. Latent learning and action-planning in robots with anticipatory classifier systems
8. YACS, a new LCS using anticipation
9. Adding a generalization mechanism to YACS
10. Learning classifier systems from a reinforcement learning perspective
11. Mining oblique data with XCS
12. Introduction to the JEDC Special Issue on Agent Based Computational Economics
13. Time series properties of an artificial stock market
14. Strength and money: An LCS approach to increasing returns
15. A roadmap to the last decade of learning classifier system research (from 1989 to 1999)
16. Extending the representation of classifier conditions, Part II: From messy coding to S-expressions
17. State of XCS classifier system research
18. Classifier systems and the animat problem
19. Strength or accuracy? Fitness calculation in learning classifier systems
20. Learning classifier systems applied to knowledge discovery in clinical research databases
21. XCS and the Monk's problems
22. Classifier systems and economic modelling
23. On using ZCS in a simulated continuous double-auction market
24. An adaptive agent based economic model
25. How XCS evolves accurate classifiers
26. What makes a problem hard for XCS?
27. A corporate XCS
28. Toward optimal classifier system performance in non-Markov environments
29. Evolutionary algorithms for reinforcement learning