Learning Classifier System with Self-adaptive Discovery Mechanism

2008 ◽  
pp. 273-283
Author(s):  
Maciej Troc ◽  
Olgierd Unold
Keyword(s):  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Self Adaptive

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.

scholarly journals Self-adaptation of parameters in a learning classifier system ensemble machine

2010 ◽  
Vol 20 (1) ◽  
pp. 157-174 ◽  
Author(s):  
Maciej Troć ◽  
Olgierd Unold
Keyword(s):  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
System A ◽  
Learning Classifier ◽  
Ensemble Machine Learning ◽  
One Step ◽  
Self Adaptation ◽  
Selection Of ◽  
Self Adaptive

Self-adaptation of parameters in a learning classifier system ensemble machineSelf-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCS-based ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.

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
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