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Deformed fuzzy automata are complex structures that can be used for solving approximate string matching problems when input strings are composed by fuzzy symbols. Different string similarity definitions are obtained by the appropriate selection of fuzzy operators and parameters involved in the calculus of the automaton transitions. In this paper, we apply a genetic algorithm to adjust the automaton parameters for selecting the ones best fit to a particular application. This genetic approach overcomes the difficulty of using common optimizing techniques like gradient descent, due to the presence of non-derivable functions in the calculus of the automaton transitions. Experimental results, obtained in a text recognition experience, validate the proposed methodology.