Skip to main content

On interactive evolutionary algorithms and stochastic mealy automata

  • Theoretical Foundations of Evolutionary Computation
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

Abstract

Interactive evolutionary algorithms (IEAs) are special cases of interactive optimization methods. Potential applications range from multicriteria optimization to the support of rapid prototyping in the field of design. In order to provide a theoretical framework to analyze such evolutionary methods, the IEAs are formalized as stochastic Mealy automata. The potential impacts of such a formalization are discussed.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Geoffrion, J. Dyer, and A. Feinberg. An interactive approach for multi-criterion optimization, with an application to the operation of an academic department. Management Science, 19:357–368, 1972.

    Google Scholar 

  2. P. Korhonen. Solving discrete multiple criteria problems by using visual interaction. In G. Fandel, M. Grauer, A. Kurzhanski, and A.P. Wierzbicki, editors, Large-Scale Modelling and Interactive Decision Analysis, pages 176–185. Springer, Berlin, 1986.

    Google Scholar 

  3. A. Lewandowski. User-machine interface and intelligent decision support. In G. Fandel, M. Grauer, A. Kurzhanski, and A.P. Wierzbicki, editors, Large-Scale Modelling and Interactive Decision Analysis, pages 161–175. Springer, Berlin, 1986.

    Google Scholar 

  4. C.M. Fonseca and P.J. Fleming. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In S. Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423. Morgan Kaufmann, San Mateo (CA), 1993.

    Google Scholar 

  5. J. Graf. Interactive evolutionary algorithms in design. In D.W. Pearson, N.C. Steele, and R.F. Albrecht, editors, Proceedings of the 2nd International Conference on Artificial Neural Networks and Genetic Algorithms, pages 227–230. Springer, Vienna, 1995.

    Google Scholar 

  6. H. Adeli and K.V. Balasubramanyam. A synergetic man-machine approach to shape optimization of structures. Computers and Structures, 30(3):553–561, 1988.

    Article  Google Scholar 

  7. W.B. Powell and Y. Sheffi. Design and implemetation of an interactive optimization system for network design in the motor carrier industry. Operations Research, 37(1):12–29, 1989.

    Google Scholar 

  8. A.E. Nix and M.D. Vose. Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence, 5:79–88, 1992.

    Article  Google Scholar 

  9. T.E. Davis and J. Principe. A markov chain framework for the simple genetic algorithm. Evolutionary Computation, 1(3):269–288, 1993.

    Google Scholar 

  10. D.B. Fogel. Asymptotic convergence properties of genetic algorithms and evolutionary programming: Analysis and experiments. Cybernetics and Systems, 25(3):389–407, 1994.

    Google Scholar 

  11. G. Rudolph. Convergence properties of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5(1):96–101, 1994.

    Article  Google Scholar 

  12. J. Suzuki. A markov chain analysis on simple genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 25(4):655–659, 1995.

    Google Scholar 

  13. A. Paz. Introduction to Probabilistic Automata. Academic Press, New York and London, 1971.

    Google Scholar 

  14. R.G. Bukharaev. Theorie der stochastischen Automaten. Teubner, Stuttgart, 1995.

    Google Scholar 

  15. M.D. Vose and G.E. Liepins. Punctuated equilibria in genetic search. Complex Systems, 5(1):31–44, 1991.

    Google Scholar 

  16. E.-E. Doberkat. Convergence theorems for stochastic automata and learning systems. Mathematical Systems Theory, 12:347–359, 1979.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rudolph, G. (1996). On interactive evolutionary algorithms and stochastic mealy automata. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_986

Download citation

  • DOI: https://doi.org/10.1007/3-540-61723-X_986

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics