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While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computing and a training data covering algorithm. ; The presented method consists of the following steps: obtaining an initial set of rules with parameterized consequences using the Michigan approach combined with an evolutionary strategy and a covering algorithm for the training data set; reducing the obtained rule base using a simple genetic algorithm; multi-phase tuning of the fuzzy inference system with parameterized consequences using the Pittsburgh approach and an evolutionary strategy. ; The paper presents experimental results using popular benchmark data sets regarding system identification and time series prediction, providing a reliable comparison to other learning methods, particularly those based on neuro-fuzzy, clustering and ?-insensitive methods. An examplary fuzzy inference system with parameterized consequences using the Reichenbach implication and the minimum t-norm was implemented to obtain numerical results.