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The genotype–phenotype encoding of fuzzy rule bases in GA, along with their corresponding crossover and mutation operators, can be used by other search schemes, improving the behavior of these last ones. As a practical consequence of this, a simulated annealing-based method for inducting both parameters and structure of a fuzzy classifier has been developed. The adjacency operator in SA has been replaced with a macromutation taken from tree-shaped genotype GAs. We will show that results of SA search are similar to those of GP in both the efficiency of the learned classifiers and in its linguistic interpretability, while the memory consumption of the learning process is lower.