The Induction of Finite Transducers Using Genetic Programming

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.

[1]  Scott Brave,et al.  Evolving deterministic finite automata using cellular encoding , 1996 .

[2]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[3]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[4]  Rafael C. Carrasco,et al.  Grammatical Inference and Applications , 1994, Lecture Notes in Computer Science.

[5]  Frederick E. Petry,et al.  Regular language induction with genetic programming , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[6]  Richard B. Bunt,et al.  An introduction to computer , 1979 .

[7]  Simon M. Lucas,et al.  Evolving Finite State Transducers: Some Initial Explorations , 2003, EuroGP.

[8]  Mikel L. Forcada,et al.  Neural Networks: Automata and Formal Models of Computation , 2002 .

[9]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

[10]  Daniel I. A. Cohen,et al.  Introduction to computer theory , 1986 .

[11]  Pierre Dupont,et al.  Regular Grammatical Inference from Positive and Negative Samples by Genetic Search: the GIG Method , 1994, ICGI.

[12]  Simon M. Lucas,et al.  Learning DFA: evolution versus evidence driven state merging , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..