Fault tolerant fusion of office sensor data using cartesian genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Bentley:2017:ieeeSSCI,

• author = "P. J. Bentley and S. L. Lim",
• booktitle = "2017 IEEE Symposium Series on Computational Intelligence (SSCI)",
• title = "Fault tolerant fusion of office sensor data using cartesian genetic programming",
• year = "2017",
• abstract = "The Smart Grid of the future will enable a cleaner, more efficient and fault tolerant system of power distribution. Sensing power use and predicting demand is an important component in the Smart Grid. In this work, we describe a Cartesian Genetic Programming (CGP) system applied to a smart office. In the building, power usage is directly proportional to the number of people present. CGP is used to perform data fusion on the data collected from smart sensors embedded in the building in order to predict the number of people over a two-month period. This is a challenging task, as the sensors are unreliable, resulting in incomplete data. It is also challenging because in addition to normal staff, the building underwent renovation during the test period, resulting the presence of additional personnel who would not normally be present. Despite these difficult real-world issues, CGP was able to learn human-readable rules that when used in combination, provide a method for data fusion that is tolerant to the observed faults in the sensors.",
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming",
• DOI = "doi:10.1109/SSCI.2017.8280827",
• month = nov,
• notes = "Also known as \cite{8280827}",

}

Genetic Programming entries for Peter J Bentley S L Lim

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