Distributed Light Brightness Control based on cuSASGP

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Ono:2016:CEC,

• author = "Keiko Ono and Yoshiko Hanada",
• title = "Distributed Light Brightness Control based on {cuSASGP}",
• booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)",
• year = "2016",
• editor = "Yew-Soon Ong",
• pages = "838--845",
• address = "Vancouver",
• month = "24-29 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, GPU",
• isbn13 = "978-1-5090-0623-6",
• DOI = "doi:10.1109/CEC.2016.7743878",
• abstract = "This paper describes an approach for controlling light luminance using the CUDA-based Self-adaptive Subpopulation Model of Genetic Programming (cuSASGP). The method involves the evolution of a genetic programming lighting control rule for the ceiling lights in an office room to satisfy different brightness requirements at each desk and reduce electric power consumption. Although the lighting control problem has many local minima, cuSASGP uses solution features to construct an appropriate island formation that avoids these local minima. Thus, an approach for controlling light luminance based on cuSASGP could be expected to improve performance in terms of avoiding local minima and genetic diversity. We first define the lighting control problem for ceiling lights, and propose a genetic programming approach. We implement five types of functional nodes and three types of terminal nodes. Moreover, we verify that genetic diversity can be achieved by adopting subpopulation models such as the island method and cuSASGP in the lighting control problem. For two different environments, we demonstrate that the proposed genetic programming approach can optimize an appropriate lighting pattern that satisfies both user requests and energy constraints, and that use of cuSASGP enhance genetic diversity.",
• notes = "WCCI2016",

}

Genetic Programming entries for Keiko Ono Yoshiko Hanada

Citations