| Title: | Replacing the genetic algorithm with multi-objective bacterial foraging optimization in XCS |
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| Authors: | ID Novak, Damijan (Author)
ID Fister, Iztok (Author)
ID Dugonik, Jani (Author) |
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| Files: |  mathematics-14-01947-v2_(1).pdf (2,47 MB)
MD5: 9E787825060664F0DB49D9FC29A953EF
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | This article is positioned into a cross-section of machine learning, cybersecurity, and natureinspired domains. This article’s main objective is to use eXtended Classifier System (XCS), a known adaptive Reinforcement Learning (RL) algorithm, and alter it to use the Bacterial Foraging Optimization Algorithm (BFOA) instead of its original Genetic Algorithm component. This modification transforms XCS into a multi-criteria optimization system (BFOA-XCS) through evaluation of classifier fitness across accuracy, stability, and variance reduction while simultaneously using weighted-sum scalarization. In this way, the method leverages BFOA’s chemotactic search and population dynamics. The proposed BFOA-XCS integration was validated in two experimental phases. First, evaluations across 19 benchmark machine learning datasets demonstrated that Improved BFOA (IBFOA)-XCS achieves the best Friedman ranking among all XCS variants (marginally significant at α = 0.10, supported by medium-to-large effect sizes), with notable variance reduction (15.2 percent) over standard GA-XCS. Second, in a dynamic cybersecurity simulation environment with six attack scenarios, all XCS variants significantly outperformed three of five deep RL baselines (Deep Q-Network (DQN), Q-Learning, and Policy Gradient (REINFORCE)) with large statistical effect sizes. Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC) achieved higher overall rewards but at substantially greater computational cost: PPO at 5.3× and SAC at 26.1× the XCS compute time per run (2 min 8 s and 10 min 26 s, respectively, vs. 24 s for XCS). The results demonstrate that rule-based XCS with BFOA optimization offers a compelling alternative to neural approaches for cybersecurity defense, combining competitive performance with interpretable policies and substantially lower computational requirements. |
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| Keywords: | association rule mining, swarm intelligence, cybersecurity, eXtended Classifier System, learning classifier systems, multi-objective optimization, bacterial foraging optimization algorithm, reinforcement learning, optimization algorithms |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 04.05.2026 |
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| Article acceptance date: | 31.05.2026 |
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| Publication date: | 02.06.2026 |
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| Publisher: | MDPI |
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| Year of publishing: | 2026 |
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| Number of pages: | 38 str. |
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| Numbering: | Vol. 14, iss. 11, [article no.] 1947 |
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| PID: | 20.500.12556/DKUM-98512  |
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| UDC: | 004.8 |
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| ISSN on article: | 2227-7390 |
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| COBISS.SI-ID: | 281207043 |
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| Copyright: | © 2026 by the authors |
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| Publication date in DKUM: | 17.06.2026 |
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| Views: | 218 |
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| Downloads: | 13 |
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| Metadata: |    |
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| Categories: | Misc.
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