Adaptive Finite State Automata and Genetic Algorithms: Merging Individual Adaptation and Population Evolution

  • H. Pistori
  • P. S. Martins
  • A. A. de CastroJr.


This paper presents adaptive finite state automata as an alternative formalism to model individuals in a genetic algorithm environment. Adaptive finite automata, which are basically finite state automata that can change their internal structures during operation, have proven to be an attractive way to represent simple learning strategies. We argue that the merging of adaptive finite state automata and GA results in an elegant and appropriate environment to explore the impact of individual adaptation, during lifetime, on population evolution.


Genetic Algorithm Adaptive Function Input Symbol State Automaton Finite State Automaton 


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  1. [1]
    Jones, M., Konstam, A. (1999) The use of genetic algorithms and neural networks to investigate the Baldwin effect. In: Proc. of the ACM symposium on Applied computing, pp. 275–279Google Scholar
  2. [2]
    Wellock, C, Ross, B. J. (2001) An examination of Lamarckian genetic algorithms. In: Erik D. Goodman (ed.) Genetic and Evolutionary Computation Conference Late Breaking Papers 2001, San Francisco, California, USA, pp. 474–481Google Scholar
  3. [3]
    Belew, R., Mitchell, M. (1996) Adaptive Individuals in Evolving Populations: Models and Algorithms-SFI Studies in the Sciences of Complexity-Vol.XXIII. Addison Wesley, BostonGoogle Scholar
  4. [4]
    Turney, P. (1996) Myths and legends of the Baldwin effect. In: Proc. of the 13th Int. Conf. on Machine Learning, pp. 135–142Google Scholar
  5. [5]
    Nolfi, N. (1999) How learning and evolution interact: The case of a learning task which differs from the evolutionary task. Adaptive Bahavior 7(2):231–236Google Scholar
  6. [6]
    Neto, J. J. (1994) Adaptive automata for context-sensitive languages. SIGPLAN Notices 29(9): 115–124CrossRefGoogle Scholar
  7. [7]
    Rubinstein, R., Shutt, J. N. (1994) Self-modifying finite automata. In: Proc. of the 13th IFIP World Computer Congress, pp. 493–498Google Scholar
  8. [8]
    Klein, A., Kutrib, M. (2002) Self-assembling fi nite automata. In: Proc. of the 8th Annual Int. Conf. on Computing and Combinatorics, pp. 310–319Google Scholar
  9. [9]
    Belz, A., Eskikaya, B. (1998) A genetic algorithm for fi nite state automata induction with an application to phonotactics. In: ESSLLI-98 Workshop on Automated Acquisition of Syntax and Parsing, pp. 9–17Google Scholar
  10. [10]
    Lankhorst, M. M. (1995) A genetic algorithm for the induction of nondeterministic pushdown automata. In: Computing Science Report CS-R 9502, University of Groningen.Google Scholar
  11. [11]
    Bertelle, C., Flouret, M., Jay, V., Olivier, D., Ponty, J. (2001) Genetic algorithms on automata with multiplicities for adaptive agent behaviour in emergenet organizations. In: Proc. of World Multicon-ference on Systemics, Cybernetics and Informatics 2001, pp. 22–25Google Scholar
  12. [12]
    Cicchello, O., Kremer, S. C. (2003) Inducing grammars from sparse data sets: A survey of algorithms and results. Journal of Machine Learning Research 4:603–632MathSciNetCrossRefGoogle Scholar
  13. [13]
    Higuera, C. De La (2001) Current trends in grammatical inference. Lecture Notes in Computer Science 1876:28–30Google Scholar
  14. [14]
    Hinton, G., Nolan, S. (1987) How learning can guide evolution. Complex Systems 1:495–502Google Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • H. Pistori
    • 1
  • P. S. Martins
    • 1
  • A. A. de CastroJr.
    • 1
  1. 1.Department of Computer EngineeringDom Bosco Catholic UniversityBrazil

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