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A Functional Optimization Method for Continuous Domains

  • Viet-Hung Dang
  • Ngo Anh VienEmail author
  • Pham Le-Tuyen
  • Taechoong Chung
Conference paper
  • 591 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 221)

Abstract

Smart city solutions are often formulated as adaptive optimization problems in which a cost objective function w.r.t certain constraints is optimized using off-the-shelf optimization libraries. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is an efficient derivative-free optimization algorithm where a black-box objective function is defined on a parameter space. This modeling makes its performance strongly depends on the quality of chosen features. This paper considers modeling the input space for optimization problems in reproducing kernel Hilbert spaces (RKHS). This modeling amounts to functional optimization whose domain is a function space that enables us to optimize in a very rich function class. Our CMA-ES-RKHS framework performs black-box functional optimization in the RKHS. Adaptive representation of the function and covariance operator is achieved with sparsification techniques. We evaluate CMA-ES-RKHS on simple functional optimization problems which are motivated from many problems of smart cities.

Keywords

Functional optimization Smart city Cross-entropy Covariance matrix adaptation evolution strategy 

Notes

Acknowledgement

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2016.18. Tuyen and Chung are funded through the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (2014R1A1A2057735).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Viet-Hung Dang
    • 1
  • Ngo Anh Vien
    • 2
    Email author
  • Pham Le-Tuyen
    • 3
  • Taechoong Chung
    • 3
  1. 1.Research and Development CenterDuy Tan UniversityDa NangVietnam
  2. 2.Machine Learning and Robotics LabUniversity of StutgartStuttgartGermany
  3. 3.Department of Computer EngineeringKyung Hee UniversitySeoulKorea

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