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Designing Fuzzy Rule Bases With A Bayesian Artificial Immune System
Proceedings Of The 2011 11th International Conference On Hybrid Intelligent Systems, His 2011. , v. , n. , p. 584 - 589, 2011.
Von Zuben F.J.
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. 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