In this paper we apply an immune-inspired approach to generate fuzzy rule bases for classification problems. Our proposal, called Bayesian Artificial Immune System (BAIS), is a hybrid algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Thus, the algorithm takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions (building blocks). Besides the capability to identify and manipulate building blocks, the algorithm maintains diversity in the population, performs multimodal optimization and adjusts the size of the population automatically according to the problem. These attributes are generally absent from alternative algorithms, and can be considered useful attributes when generating fuzzy rule bases, thus guiding to high-performance classifiers. BAIS was evaluated in six well-known classification problems and its performance compares favorably with that produced by contenders. © 2011 IEEE. 584589Broom, B., Subramanian, D., Computational Methods for Learning Bayesian Networks from High-Throughput Biological Data (2006) Bayesian Inference for Gene Expression and Proteomics, , Cambridge UniversityCastro, P.D., Coelho, G.P., Caetano, M.F., Von Zuben, F.J., Designing ensembles of fuzzy classification systems: An immune-inspired approach (2005) Lecture Notes in Computer Science, 3627, pp. 469-482. , Artificial Immune Systems: 4th International Conference, ICARIS 2005. ProceedingsCastro, P.A.D., Von Zuben, F.J., Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System (2011) IEEE Trans. on Neural Networks, 22 (2), pp. 304-316Castro, P.A.D., Von Zuben, F.J., BAIS: A Bayesian Artificial Immune System for Effective Handling of Building Blocks (2009) Information Sciences - Special Issue on Artificial Immune Systems, 179, pp. 1426-1440Castro, P.A.D., Von Zuben, F.J., Feature subset selection by means of a Bayesian artificial immune system (2008) Proc. of the 8th Int. Conf. on Hybrid Intelligent Systems, pp. 561-566Castro, P.A.D., Von Zuben, F.J., MOBAIS: A Bayesian artificial immune system for multi-objective optimization (2008) Lecture Notes in Computer Science, 5132, pp. 48-59. , SpringerCastro, P.A.D., Von Zuben, F.J., Multi-objective Bayesian artificial immune system: Empirical evaluation and comparative analyses (2009) Journal of Mathematical Modelling and Algorithms, pp. 151-173Castro, P.A.D., Von Zuben, F.J., Multi-objective feature selection using a Bayesian artificial immune system (2010) Journal of Intelligent Computing and Cybernetics, 3, pp. 235-256. , noCastro, P.A.D., Von Zuben, F.J., GAIS: A Gaussian Artificial Immune System for Continuous Optimization Proc. of the 9th Int. Conf. on Artificial Immune Systems, 2010Castro, P.A.D., Von Zuben, F.J., A Gaussian Artificial Immune System for Multi-Objective Optimization in Continuous Domains (2010) Proc. of the 10th Int. Conf. on Hybrid Intelligent Systems, pp. 159-164. , Atlanta, USACastro, P.A.D., Von Zuben, F.J., Training multilayer perceptrons with a Gaussian Artificial Immune System (2011) Proc. of the Congress on Evolutionary Computation (CEC), pp. 1250-1257Cooper, G., Herskovits, E., A Bayesian method for the induction of probabilistic networks from data (1992) Machine Learning, 9, pp. 309-347Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L., Genetic Fuzzy Systems. Evolutionary tuning and learning of fuzzy knowledge bases (2001) Advances in Fuzzy Systems: Applications and Theory, 19. , World ScientificCordón, O., Del Jesus, M.J., Herrera, F., A proposal on reasoning methods in fuzzy rule-based classification systems (2001) Int. J. of Approximate Reasoning, 20, pp. 21-45De Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Trans. Evolutionary Computation, 6 (3), pp. 239-251DelaOssa, L., Gamez, J.A., Puerta, J.M., Learning weighted linguistic fuzzy rules with estimation of distribution algorithms (2006) Proc. of the IEEE Congress on Evolutionary Computation (CEC), pp. 900-907De França, F.O., Coelho, G.P., Castro, P.A.D., Von Zuben, F.J., Conceptual and Practical Aspects of the aiNet Family of Algorithms (2010) International Journal of Natural Computing Research, pp. 1-35Frank, A., Asuncion, A., (2010) UCI Machine Learning Repository, , http://archive.ics.uci.edu/ml, School of Information and Computer SciencesGacto, M.J., Alcalá, R., Herrera, F., Interpretability of Linguistic Fuzzy Rule-Based Systems: An Overview of Interpretability Measures (2011) Information Sciences, 181 (20), pp. 4340-4360Heckerman, D., Geiger, D., Chickering, D.M., Learning Bayesian networks: The combination of knowledge and statistical data (1995) Machine Learning, 20 (3), pp. 197-243Henrion, M., Propagating uncertainty in Bayesian networks by probabilistic logic sampling (1988) Uncertainty in Artificial Intelligence, 2, pp. 149-163Holland, J.H., (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, , The MIT PressJuang, C.-F., Chang, P.-H., Designing Fuzzy-Rule-Based Systems Using Continuous Ant-Colony Optimization (2010) IEEE Trans. on Fuzzy Systems, 18, pp. 138-149Klir, G., Yuan, B., (1995) Fuzzy Sets and Fuzzy Logic - Theory and Applications, , Prentice-HallPedrycz, W., Gomide, F., (1998) An Introduction to Fuzzy Sets: Analysis and Design, , MIT PressPelikan, M., Goldberg, D.E., Cantú-Paz, E., BOA: The Bayesian optimization algorithm (1999) Proc. of the Genetic and Evolutionary Computation Conference, pp. 525-532Pelikan, M., Goldberg, D., Lobo, F., A survey of optimization by building and using probabilistic models (2002) Comput. Optim. Appl., 21, pp. 5-20. , noSharma, K.D., Chatterjee, A., Rakshit, A., A Hybrid Approach for Design of Stable Adaptive Fuzzy Controllers Employing Lyapunov Theory and Particle Swarm Optimization (2009) IEEE Trans. on Fuzzy Systems, 17 (2), pp. 329-342