A direct search algorithm based on kernel density estimator for nonlinear optimization

Yiu Ming CHEUNG, Fangqing Gu

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a direct search algorithm based on kernel density estimator for the nonlinear optimization problems. It estimates the objective function by the kernel density estimator with the local samples only, and then approximates the ascent direction of the objective function with the one of the estimator. The proposed optimization approach features the derivative-free with much likely generating an ascent direction. We not only theoretically show that the search direction, which is used in the proposed algorithm towards maximizing the objective function, is the ascent direction of the objective function, but also empirically investigate the effectiveness of the search direction.

Original languageEnglish
Title of host publication2014 10th International Conference on Natural Computation, ICNC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages297-302
Number of pages6
ISBN (Electronic)9781479951505
DOIs
Publication statusPublished - 2014
Event2014 10th International Conference on Natural Computation, ICNC 2014 - Xiamen, China
Duration: 19 Aug 201421 Aug 2014

Publication series

Name2014 10th International Conference on Natural Computation, ICNC 2014

Conference

Conference2014 10th International Conference on Natural Computation, ICNC 2014
Country/TerritoryChina
CityXiamen
Period19/08/1421/08/14

Scopus Subject Areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering

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