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An Efficient XCS-based Algorithm for Learning Classifier Systems in Real Environments

Publish place: Journal of Artificial Intelligence & Data Mining، Vol: 11، Issue: 1
Publish Year: 1402
Type: Journal paper
Language: English
View: 383

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Link to this Paper:
https://civilica.com/doc/1627472

Document National Code:

JR_JADM-11-1_002

Index date: 9 April 2023

An Efficient XCS-based Algorithm for Learning Classifier Systems in Real Environments abstract

Recently, learning classifier systems are used to control physical robots, sensory robots, and intelligent rescue systems. The most important challenge in these systems, which are models of real environments, is its non-markov quality. Therefore, it is necessary to use memory to store system states in order to make decisions based on a chain of previous states. In this research, a memory-based XCS is proposed to help use more effective rules in classifier by identifying efficient rules. The proposed model was implemented on five important maze maps and led to a reduction in the number of steps to reach the goal and also an increase in the number of successes in reaching the goal in these maps.

An Efficient XCS-based Algorithm for Learning Classifier Systems in Real Environments Keywords:

learning classifier systems (LCS) , XCS algorithm , identification of cycle and overlapping

An Efficient XCS-based Algorithm for Learning Classifier Systems in Real Environments authors

Ali Yousefi

Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Kambiz Badie

Content & E-Services Research Group, IT Research Faculty, ICT Research Institute, Tehran, Iran.

Mohammad Mehdi Ebadzadeh

Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran.

Arash Sharifi

Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

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