Balancing Specificity and Generality in a Panmictic-Based Rule-Discovery Learning Classifier System

2003 ◽  
pp. 1-19
Author(s):  
William N. L. Browne
Keyword(s):  
Discovery Learning ◽  
Rule Discovery ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier

scholarly journals Is Gradient Descent Update Consistent with Accuracy-Based Learning Classifier System?

2009 ◽  
Vol 13 (6) ◽  
pp. 640-648
Author(s):  
Atsushi Wada ◽  
◽  
Keiki Takadama ◽  
◽  
Keyword(s):  
Adaptive Systems ◽  
Gradient Descent ◽  
Benchmark Problems ◽  
Gradient Term ◽  
Rule Discovery ◽  
Discovery Process ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier

Learning Classifier Systems (LCSs) are rule-based adaptive systems that have both Reinforcement Learning (RL) and rule-discovery mechanisms for effective and practical online learning. An analysis of the reinforcement process of XCS, one of the currently mainstream LCSs, is performed from the aspect of RL. Upon comparing XCS's update method with gradient-descent-based parameter update in RL, differences are found in the following elements: (1) residual term, (2) gradient term, and (3) payoff definition. All possible combinations of the variants in each element are implemented and tested on multi-step benchmark problems. This revealed that few specific combinations work effectively with XCS's accuracy-based rule-discovery process, while pure gradient-descent-based update showed the worst performance.

ZCS Redux

2002 ◽  
Vol 10 (2) ◽  
pp. 185-205 ◽  
Author(s):  
Larry Bull ◽  
Jacob Hurst
Keyword(s):  
Genetic Algorithms ◽  
Difference Equation ◽  
Optimal Performance ◽  
Learning Classifier Systems ◽  
Rule Discovery ◽  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Simple Difference

Learning classifier systems traditionally use genetic algorithms to facilitate rule discovery, where rule fitness is payoff based. Current research has shifted to the use of accuracy-based fitness. This paper re-examines the use of a particular payoff-based learning classifier system—ZCS. By using simple difference equation models of ZCS, we show that this system is capable of optimal performance subject to appropriate parameter settings. This is demonstrated for both single- and multistep tasks. Optimal performance of ZCS in well-known, multistep maze tasks is then presented to support the findings from the models.

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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