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Journal of Artificial Intelligence and Data Mining، جلد ۱۱، شماره ۱، صفحات ۱۳-۲۷
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عنوان انگلیسی An Efficient XCS-based Algorithm for Learning Classifier Systems in Real Environments
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله learning classifier systems (LCS), XCS algorithm, identification of cycle and overlapping
نویسندگان مقاله 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

نشانی اینترنتی https://jad.shahroodut.ac.ir/article_2679_1db19f86540245b782d97833bf465409.pdf
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