Learning Finite-State Transducers: Evolution Versus Heuristic State Merging

@article{Lucas2007LearningFT,
  title={Learning Finite-State Transducers: Evolution Versus Heuristic State Merging},
  author={Simon M. Lucas and T. Jeff Reynolds},
  journal={IEEE Transactions on Evolutionary Computation},
  year={2007},
  volume={11},
  pages={308-325}
}
Finite-state transducers (FSTs) are finite-state machines (FSMs) that map strings in a source domain into strings in a target domain. While there are many reports in the literature of evolving FSMs, there has been much less work on evolving FSTs. In particular, the fitness functions required for evolving FSTs are generally different from those used for FSMs. In this paper, three string distance-based fitness functions are evaluated, in order of increasing computational complexity: string… CONTINUE READING

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