Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Deformed systems for contextual postprocessing
Fuzzy Sets and Systems
Lexicon-Driven Handwritten Word Recognition Using Optimal Linear Combinations of Order Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computation of Normalized Edit Distance and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformed fuzzy automata for correcting imperfect strings of fuzzy symbols
IEEE Transactions on Fuzzy Systems
Comparison of crisp and fuzzy character neural networks in handwritten word recognition
IEEE Transactions on Fuzzy Systems
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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.