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Improving the efficiency of the XCS learning classifier system using evolutionary memory

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Abstract

Recently, learning classifier systems (LCS) have been used for a variety of Internet of Things (IoT) devices and multi-cloud services, including cloud-based centralized control of physical robots and actuators in continuous-time environments. Performance analysis of sensors, navigation of humanoid robots and intelligent control of rescue systems. In these systems, we can run evolutionary or intuitive algorithms on cloud servers to search the space of rules and simultaneously other learning processes to assign how to interact with the environment to the rules in the classification. Also, the problem of continuous congestion, traffic reduction, network routing and predicting traffic conditions in wireless networks is the main challenge facing these systems in real environments. Usually such systems are non-Markovian. Therefore, they need memory to save system states. This paper presents a framework for XCS-based memory-based LCS. In addition to identifying optimal rules in overlapping modes, the XCS architecture is equipped with a memory. Memory stores the most efficient classifier rules. These rules reduce the steps to reach the goal. In the first proposed method, only those rules that affect the moving motion are kept in memory. As the number of rules increases, some of them are deleted according to memory space. In the second proposed method, some features are added to the rules of this memory and the performance of the memory is optimized using evolutionary algorithms, which are used to remove the less used rules. The relative success of the proposed LCS architecture in solving well-known maze problems compared to the conventional XCS architecture confirms its efficiency in increasing the number of successes and reducing the steps to reach the goals. The results of this research are suggested as a suitable solution for reducing routing time, reducing network load in the problem of congestion and traffic in solving problems related to wireless networks.

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Abbreviations

LCS:

Learning classifier systems

XCS:

Accuracy base learning classifier systems

GA:

Genetic algorithm

DGP:

Dynamic genetic programming

ZCS:

A zeroth level classifier system

CS-1:

Cognitive system one

XCSF:

Classifier that approximate functions

AIS:

Artificial immune systems

IoT:

Internet of things

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Acknowledgements

Authors thank editor and reviewers for their time and consideration.

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AY, KB, MME, and AS have participated in design of the proposed method and practical implementation. AY has coded the method. AY and KB have completed the first draft of this paper. All authors have read and approved the manuscript.

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Correspondence to Kambiz Badie.

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Yousefi, A., Badie, K., Ebadzadeh, M.M. et al. Improving the efficiency of the XCS learning classifier system using evolutionary memory. Wireless Netw 30, 5171–5186 (2024). https://doi.org/10.1007/s11276-023-03228-5

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