Modelling (sub)string-length based constraints through a grammatical inference method
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Regular Grammatical Inference from Positive and Negative Samples by Genetic Search: the GIG Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
On the Synthesis of Finite-State Machines from Samples of Their Behavior
IEEE Transactions on Computers
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Grammatical inference is the problem of inferring a grammar, given a set of positive samples which the inferred grammar should accept and a set of negative samples which the grammar should not accept. Here we apply genetic algorithm for inferring regular languages. The genetic search is started from maximal canonical automaton built from structurally complete sample. In view of limiting the increasing complexity as the sample size grows, we have edited structurally complete sample. We have tested our algorithm for 16 languages and have compared our results with previous works of regular grammatical inference using genetic algorithm. The results obtained confirm the feasibility of applying genetic algorithm for regular grammatical inference.