TY - JOUR ID - 2679 TI - An Efficient XCS-based Algorithm for Learning Classifier Systems in Real Environments JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Yousefi, Ali AU - Badie, Kambiz AU - Ebadzadeh, Mohammad Mehdi AU - Sharifi, Arash AD - Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. AD - Content & E-Services Research Group, IT Research Faculty, ICT Research Institute, Tehran, Iran. AD - Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran. AD - Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Y1 - 2023 PY - 2023 VL - 11 IS - 1 SP - 13 EP - 27 KW - learning classifier systems (LCS) KW - XCS algorithm KW - identification of cycle and overlapping DO - 10.22044/jadm.2022.12358.2384 N2 - 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. UR - https://jad.shahroodut.ac.ir/article_2679.html L1 - https://jad.shahroodut.ac.ir/article_2679_1db19f86540245b782d97833bf465409.pdf ER -