Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Internal modelling of objective functions for global optimization

  • 51 Accesses

  • 7 Citations

Abstract

Global optimization requires an adequate internal representation of the objective function for success in a reasonable number of function evaluations. A method for determining the location of a new function evaluation, based on a representation using a stationary stochastic process model, is investigated and some results are given.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Dixon, L. C. W., andSzëgo, G. P., Editors,Toward Global Optimization 2, North-Holland, Amsterdam, Holland, 1978.

  2. 2.

    Walsh, G. R.,Methods of Optimization, Wiley, London, England, 1985.

  3. 3.

    Schagen, I. P.,Stochastic Interpolating Functions—Applications in Optimization, Journal of the Institute of Mathematics and Its Applications, Vol. 26, pp. 93–101, 1980.

  4. 4.

    Schagen, I. P.,Sequential Exploration of Unknown Multidimensional Functions as an Aid to Optimization, IMA Journal of Numerical Analysis, Vol. 4, pp. 337–347, 1984.

  5. 5.

    De Biase, L., andFrontini, F.,A Stochastic Method for Global Optimization, Comptat 1978, Physica-Verlag, Vienna, Austria, pp. 355–361, 1978.

  6. 6.

    Robinson, P. J., Pettitt, A. N., Zornig, J., andBass, L.,A Bayesian Analysis of Capillary Heterogeneity in the Intact Pig Liver, Biometrics, Vol. 39, pp. 61–69, 1983.

Download references

Author information

Additional information

Communicated by R. A. Tapia

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Schagen, I.P. Internal modelling of objective functions for global optimization. J Optim Theory Appl 51, 345–353 (1986). https://doi.org/10.1007/BF00939829

Download citation

Key Words

  • Global optimization
  • stochastic process models
  • multidimensional objective functions