Computer Processing of Line-Drawing Images
ACM Computing Surveys (CSUR)
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Proceedings of the European Conference on Genetic Programming
The Data Driven Approach Applied to the OSTIA Algorithm
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Automatic induction of finite state transducers for simple phonological rules
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
Learning Deterministic Finite Automata with a Smart State Labeling Evolutionary Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
The induction of finite transducers using genetic programming
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Using genetic programming for turing machine induction
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Gene expression programming for induction of finite transducer
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Finite state machine induction using genetic algorithm based on testing and model checking
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Genetic algorithm for induction of finite automata with continuous and discrete output actions
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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Finite state transducers (FSTs) are finite state machines that map strings in a source domain into strings in a target domain. While there are many reports in the literature of evolving general finite state machines, there has been much less work on evolving FSTs. In particular, the fitness functions required for evolving FSTs are generally different to those used for FSMs. This paper considers three string-distance based fitness functions. We compute their fitness distance correlations, and present results on using two of these (Strict and Hamming) to evolve FSTs. We can control the difficulty of the problem by the presence of short strings in the training set, which make the learning problem easier. In the case of the harder problem, the Hamming measure performs best, while the Strict measure performs best on the easier problem.