Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System

2007 ◽  
pp. 59-79 ◽  
Author(s):  
Jaume Bacardit ◽  
Josep Maria Garrell
Keyword(s):  
Minimum Description Length ◽  
Minimum Description Length Principle ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier

Misclassification detection based on conditional VAE for rule evolution in learning classifier system

2021 ◽  
Author(s):  
Hiroki Shiraishi ◽  
Masakazu Tadokoro ◽  
Yohei Hayamizu ◽  
Yukiko Fukumoto ◽  
Hiroyuki Sato ◽  
...  
Keyword(s):  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier

SELF-ADAPTIVE LEARNING CLASSIFIER SYSTEM

2010 ◽  
Vol 19 (01) ◽  
pp. 275-296 ◽  
Author(s):  
OLGIERD UNOLD
Keyword(s):  
Mutation Rate ◽  
Adaptive Learning ◽  
Adaptive Mutation ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Adaptive Parameters ◽  
Proposed Model ◽  
Fixed Parameter ◽  
Self Adaptive

This article introduces a new kind of self-adaptation in discovery mechanism of learning classifier system XCS. Unlike the previous approaches, which incorporate self-adaptive parameters in the representation of an individual, proposed model evolves competitive population of the reduced XCSs, which are able to adapt both classifiers and genetic parameters. The experimental comparisons of self-adaptive mutation rate XCS and standard XCS interacting with 11-bit, 20-bit, and 37-bit multiplexer environment were provided. It has been shown that adapting the mutation rate can give an equivalent or better performance to known good fixed parameter settings, especially for computationally complex tasks. Moreover, the self-adaptive XCS is able to solve the problem of inappropriate for a standard XCS parameters.

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