20.500.12556/DKUM-98512 Replacing the genetic algorithm with multi-objective bacterial foraging optimization in XCS 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. association rule mining swarm intelligence cybersecurity eXtended Classifier System learning classifier systems multi-objective optimization bacterial foraging optimization algorithm reinforcement learning optimization algorithms asociacijsko pravilo rudarjenja umetna inteligenac kibernetska varnost optimizacijski algoritmi true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/mathematics © 2026 by the authors Članek v reviji 2026-06-17 15:43:45 2026-06-17 15:43:46 2026-06-18 03:25:30 0000-00-00 00:00:00 2026 0 0 38 str. iss. 11, [article no.] 1947 Vol. 14 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2026-05-04 2026-05-31 2026-06-02 004.8 281207043 2227-7390 mathematics-14-01947-v2_(1).pdf mathematics-14-01947-v2_(1).pdf 1 9E787825060664F0DB49D9FC29A953EF bb73cf3a46d3a94185cd751b12569f1d752a8192aa40b99f43edd5c95045f3de d39c1138-6a52-11f1-87c9-00155d580e34 https://dk.um.si/Dokument.php?lang=slv&id=207255 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0