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.
Similar content being viewed by others
Availability of data and materials
Not applicable.
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
References
Gowri, A. S., ShanthiBala, P., & Ramdinthara, I. Z. (2022). Fog-cloud enabled internet of things using extended classifier system (XCS). In S. Pal, D. De, & R. Buyya (Eds.), Artificial intelligence-based internet of things systems (pp. 163–189). Cham: Springer.
Qi, L., Lin, W., Zhang, X., Dou, W., Xu, X., & Chen, J. (2022). A correlation graph based approach for personalized and compatible web APIs recommendation in mobile APP development. IEEE Transactions on Knowledge and Data Engineering 1–1.
Chen, Y., Zhao, F., Lu, Y., & Chen, X. (2022). Dynamic task offloading for mobile edge computing with hybrid energy supply. Tsinghua Science and Technology 28(3), 421–432.
Yuan, L., et al. (2021). CSEdge: enabling collaborative edge storage for multi-access edge computing based on blockchain. IEEE Transactions on Parallel and Distributed Systems, 33(8), 1873–1887.
Xu, X., et al. (2022). DisCOV: Distributed COVID-19 detection on X-ray images with edge-cloud collaboration. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2022.3142265
Xu, J., Li, D., Gu, W., & Chen, Y. (2022). UAV-assisted task offloading for IoT in smart buildings and environment via deep reinforcement learning. Building and Environment, 222, 109218.
Chen, Y., Zhao, F., Chen, X., & Wu, Y. (2021). Efficient multi-vehicle task offloading for mobile edge computing in 6G networks. IEEE Transactions on Vehicular Technology.
Qi, L., et al. (2022). Data-driven web APIs recommendation for building web applications. IEEE Transactions on Big Data, 8(3), 685–698.
Kou, H. et al. (2021). Building trust/distrust relationships on signed social network through privacy-aware link prediction. Applied Soft Computing, 100, Article No. 106942.
Abbasi, M., Mohammadi Pasand, E., & Khosravi, M. R. (2020). Workload allocation in iot-fog-cloud architecture using a multi-objective genetic algorithm. Journal of Grid Computing, 18, 43–56.
Bull, L. (2011). Towards a mapping of modern AIS and LCS. In P. Liò, G. Nicosia, & T. Stibor (Eds.), Artificial immune systems (Vol. 6825, pp. 371–382). Springer.
Wilson, S. (1987). Classifier systems and the animat problem. Machine Learning, 2, 199–228.
Wilson, S. W. (1985). Knowledge growth in an artificial animal. Presented at the proceedings of the 1st international conference on genetic algorithms.
Farley, B. G., & Clark, W. (1954). Simulation of self-organizing systems by digital computer. Information Theory, Transactions of the IRE Professional Group on, 4, 76–84.
Shannon, C. E. (1988). Programming a computer for playing chess. In L. David (Ed.), Computer chess compendium (pp. 2–13). Springer.
Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3, 210–229.
Samuel, A. L. (1967). Some studies in machine learning using the game of checkers. II—Recent progress. IBM Journal of Research and Development., 11(6), 601–617.
Holland, J. H. (1985). Properties of the bucket brigade. Presented at the proceedings of the 1st international conference on genetic algorithms.
Box, G. E. P. (1957). Evolutionary operation: A method for increasing industrial productivity. Applied Statistics: A Journal of the Royal Statistical Society, 6, 81–101.
Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (2000). Learning classifier systems: From foundations to applications (Vol. 1813, p. 354). Springer.
Holland, J., Booker, L., Colombetti, M., Dorigo, M., Goldberg, D., Forrest, S., et al. (2000). What is a learning classifier system?". In P. Lanzi, W. Stolzmann, & S. Wilson (Eds.), Learning classifier systems (Vol. 1813, pp. 3–33). Springer.
Wilson, S. W., & Goldberg, D. E. (1989). A critical review of classifier systems. Presented at the proceedings of the 3rd international conference on genetic algorithms.
Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment. University of Michigan.
Booker, L. B. (1985) Improving the performance of genetic algorithms in classifier systems. Presented at the proceedings of the 1st international conference on genetic algorithms.
Booker, L. B. (1988). Classifier systems that learn internal world models. Machine Learning, 3, 161–192.
Booker, L. B. (1989). Triggered rule discovery in classifier systems. Presented at the proceedings of the 3rd international conference on genetic algorithms.
Wilson, S. W. (1994). Zcs: A zeroth level classifier system. Evolutionary Computation, 2, 1–18.
Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88, 135–170.
Wilson, S. W. (2002). Classifiers that approximate functions, vol. 1, pp. 211–234.
Lanzi, P. L. (1999). An analysis of generalization in the XCS classifier system. Evolutionary Computation, 7, 125–149.
Butz, M. V., Kovacs, T., Lanzi, P. L., & Wilson, S. W. (2004). Toward a theory of generalization and learning in XCS. IEEE Transactions on Evolutionary Computation, 8, 28–46.
Bull, L. (2015). A brief history of learning classifier systems: from CS-1 to XCS and its variants. Evolutionary Intelligence, 8(2), 55–70.
Hamzeh, A., Hashemi, S., Sami, A., & Rahmani, A. (2009). A recursive classifier system for partially observable environments. Fundamenta Informaticae, 97, 15–40.
Hamzeh, A., & Rahmani, A. (2008). A new architecture for learning classifier systems to solve POMDP problems. Fundamenta Informaticae, 84, 329–351.
Preen, R., & Bull, L. (2014). Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system. Soft Computing, 18, 153–167.
Zouri, M., & Ferworn, A. (2022) An approach for automatic discovery of rules based on ECG data using learning classifier systems. In 2022 IEEE world AI IoT congress (AIIoT). IEEE.
Yazdani, N. M., & Seqeloo, A. Y. (2014). Diabetes diagnosis via XCS classifier system. Frontiers in Health Informatics, 3(1), 1–8.
Huang, P.-H., et al. (2022). Toward evaluating critical factors of extubation outcome with XCSR-generated rules. Bioengineering, 9(11), 701.
Farajollahi, B., et al. (2021). Diabetes diagnosis using machine learning. Frontiers in Health Informatics, 10(1), 65.
Owens, J. et al. (2022). Interpretable convolutional learning classifier system (C-LCS) for higher dimensional datasets. In 2022 IEEE international conference on systems, man, and cybernetics (SMC). IEEE.
Zang, Z., Li, D., & Wang, J. (2015). Learning classifier systems with memory condition to solve non-Markov problems. Soft Computing, 19, 1679–1699.
Bull, L. (2005). Two simple learning classifier systems. In L. Bull & T. Kovacs (Eds.), Foundations of learning classifier systems (Vol. 183, pp. 63–89). Springer.
Acknowledgements
Authors thank editor and reviewers for their time and consideration.
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
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.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
Authors' information
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-023-03228-5