Acquisition of a specialty in multi-agent learning: approach from learning classifier system

2004 ◽  
Author(s):  
H. Inoue ◽  
K. Shimohara ◽  
K. Takadama ◽  
O. Katai
Keyword(s):  
Learning Approach ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Agent Learning ◽  
Multi Agent

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.

Sign in / Sign up

Export Citation Format

Share Document