Improving CMA-ES by random evaluation on the minor eigenspace
Refereed conference paper presented and published in conference proceedings

Times Cited
Altmetrics Information

Other information
AbstractThis paper proposes a modification to the covariance matrix adaptation evolution strategies (CMA-ES). The goal of our modification is to reduce the number of function evaluations to adapt the covariance matrix to the optimal one when the standard CMA-ES is used to optimize convex-quadratic objective functions which have repeated or clustered eigenvalues in their Hessian matrices. By randomly evaluating the minor eigenspace, the modified CMA-ES is evaluated on a standard suite of benchmark problems and its performance is compared with that of the standard CMA-ES. The experimental results show that our proposed modification can improve the performance of the CMA-ES when dominant eigenspaces and minor eigenspaces exist in the Hessian matrices of the underlying objective functions. © 2010 IEEE.
All Author(s) ListAu C.-K., Leung H.-F.
Name of Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Start Date of Conference18/07/2010
End Date of Conference23/07/2010
Place of ConferenceBarcelona
Country/Region of ConferenceSpain
LanguagesEnglish-United Kingdom

Last updated on 2021-26-02 at 00:40