Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings

Research Article

A Functional Optimization Method for Continuous Domains

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  • @INPROCEEDINGS{10.1007/978-3-319-74176-5_22,
        author={Viet-Hung Dang and Ngo Vien and Pham Le-Tuyen and Taechoong Chung},
        title={A Functional Optimization Method for Continuous Domains},
        proceedings={Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings},
        proceedings_a={INISCOM},
        year={2018},
        month={1},
        keywords={Functional optimization Smart city Cross-entropy Covariance matrix adaptation evolution strategy},
        doi={10.1007/978-3-319-74176-5_22}
    }
    
  • Viet-Hung Dang
    Ngo Vien
    Pham Le-Tuyen
    Taechoong Chung
    Year: 2018
    A Functional Optimization Method for Continuous Domains
    INISCOM
    Springer
    DOI: 10.1007/978-3-319-74176-5_22
Viet-Hung Dang1, Ngo Vien2,*, Pham Le-Tuyen3, Taechoong Chung3
  • 1: Duy Tan University
  • 2: University of Stutgart
  • 3: Kyung Hee University
*Contact email: vien.ngo@gmail.com

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.