Abstract
This paper reports on the results of a preliminary study conducted to evaluate genetic programming (GP) as a means of evolving finite state transducers. A genetic programming system representing each individual as a directed graph was implemented to evolve Mealy machines. Tournament selection was used to choose parents for the next generation and the reproduction, mutation and crossover operators were applied to the selected parents to create the next generation. The system was tested on six standard Mealy machine problems. The GP system was able to successfully induce solutions to all six problems. Furthermore, the solutions evolved were human-competitive and in all cases the minimal transducer was evolved.
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References
Koza, J.R.: Genetic Programming I: On the Programming of Computers by Natural Selection. MIT Press, Cambridge (1992)
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley and Sons, New York (1966)
Dupont, P.: Regular Grammatical Inference from Positive and Negative Samples by Genetic Search: the GIG Method. In: Carrasco, R.C., Oncina, J. (eds.) Grammatical Inference and Applications. LNCS, vol. 862, pp. 236–245. Springer, Heidelberg (1994)
Dunay, B.D., Petry, F.E., Buckles, B.P.: Regular Language Induction with Genetic Programming. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, Orlando, Florida, USA, pp. 396–400. IEEE Press, Los Alamitos (1994)
Brave, S.: Evolving Deterministic Finite Automata Using Cellular Encoding. In: Koza, J.R., et al. (eds.) Proceedings of the First Annual Conference on Genetic Programming (GP 96), pp. 39–44. MIT Press, Cambridge (1996)
Luke, S., Hamahashi, S., Kitano, H.: “Genetic” Programming. In: Banzhaf, W., et al. (eds.) Proceedings of the Genetic Programming and Evolutionary Computation Conference, vol. 2, Orlando, Florida, USA, pp. 1098–1105. Morgan Kaufmann, San Francisco (1999)
Lucas, S.M., Reynolds, T.: Learning DFA: Evolution versus Evidence Driven State Merging. In: The Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), pp. 351–358. IEEE Press, New York (2003)
Lucas, S.M.: Evolving Finite State Transducers: Some Initial Explorations. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 14–16. Springer, Heidelberg (2003)
Cohen, D.I.A.: Introduction to Computer Theory. John Wiley & Sons, New York (1986)
Forcada, M.L.: Neural Networks: Automata and Formal Methods of Computation (January 2002), http://www.dlsi.ua.es/~mlf/nnafmc/pbook.pdf
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Naidoo, A., Pillay, N. (2007). The Induction of Finite Transducers Using Genetic Programming. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_35
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DOI: https://doi.org/10.1007/978-3-540-71605-1_35
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