Advertisement

From Syntactical to Semantical Mutation Operators for Structure Optimization

  • Dirk Wiesmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

The optimization of structures is important for many industrial applications. But the problem of structure optimization is hardly understood. In the field of evolutionary computation mostly syntactical (pure structure-based) variation operators are used. For this kind of variation operators it is difficult to integrate domain-knowledge and to control the size of a mutation step. To gain insight into the basic problems of structure optimization we analyze mutation operators for evolutionary programming. For a synthetic problem we are able to derive a semantical mutation operator. The semantical mutation operator makes use of domain knowledge and has a well-defined parameter to adjust the step size.

Keywords

Variation Operator Structure Optimization Evolutionary Computation Mutation Operator Regular Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    K. Chellapilla. Evolving computer programs without subtree crossover. IEEE Transactions on Evolutionary Computation, 1(3):209–216, 1997.CrossRefGoogle Scholar
  2. 2.
    P. J. Denning, J. B. Dennis, and J. E. Qualitz. Machines, Languages, and Computation. Prentice-Hall, Englewood Cliffs, 1979.Google Scholar
  3. 3.
    S. Droste and D. Wiesmann. Metric based evolutionary algorithms. In R. Poli, W. Banzhaf, W. B. Langdon, J. F. Miller, P. Nordin, and T. C. Fogarty, editors, Genetic Programming, Proc. of EuroGP’2000, Edinburgh, April 15-16, 2000, volume 1802 of LNCS, pages 29–43, Berlin, 2000. Springer.Google Scholar
  4. 4.
    M. Emmerich, M. Grötzner, and M. Schütz. Design of graph-based evolutionary algorithms: A case study of chemical process networks. Evolutionary Computation,9(3):329–354, 2001.CrossRefGoogle Scholar
  5. 5.
    D. B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, New York, 1995.Google Scholar
  6. 6.
    L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. Wiley, New York, 1966.zbMATHGoogle Scholar
  7. 7.
    M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, New York, 1979.zbMATHGoogle Scholar
  8. 8.
    G. Rudolph. Convergence Properties of Evolutionary Algorithms. Verlag Dr. Kovač, Hamburg, 1997.Google Scholar
  9. 9.
    G. Rudolph. Finite Markov chain results in evolutionary computation: A tour d’horizon. Fundamenta Informaticae, 35(1–4):67–89, 1998.zbMATHMathSciNetGoogle Scholar
  10. 10.
    B. Sendhoff. Evolution of Structures: Optimization of Artificial Neural Structures for Information Processing. Shaker, Aachen, 1998.Google Scholar
  11. 11.
    T. Slawinski, A. Krone, U. Hammel, D. Wiesmann, and P. Krause. A hybrid evolutionary search concept for data-based generation of relevant fuzzy rules in high dimensional spaces. In Proc. of the Eighth Int’l Conf. on Fuzzy Systems (FUZZ-IEEE’99), Seoul, Korea, Aug. 22–25, 1999, pages 1431–1437, Piscataway, NJ, 1999. IEEE Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Dirk Wiesmann
    • 1
  1. 1.FB Informatik, LS 11Univ. DortmundDortmundGermany

Personalised recommendations