Multi-objective learning of white box models with low quality data

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@Article{Villar2012219,
  author =       "Jose R. Villar and Alba Berzosa and 
                 Enrique {de la Cal} and Javier Sedano and Marco Garcia-Tamargo",
  title =        "Multi-objective learning of white box models with low
                 quality data",
  journal =      "Neurocomputing",
  volume =       "75",
  number =       "1",
  pages =        "219--225",
  year =         "2012",
  note =         "Brazilian Symposium on Neural Networks (SBRN 2010)
                 International Conference on Hybrid Artificial
                 Intelligence Systems (HAIS 2010)",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2011.02.025",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231211004115",
  keywords =     "genetic algorithms, genetic programming, Low quality
                 data, Multi-objective simulated annealing, Energy
                 efficiency",
  abstract =     "Improving energy efficiency in buildings represents
                 one of the main challenges faced by engineers. In
                 fields like lighting control systems, the effect of low
                 quality sensors compromises the control strategy and
                 the emergence of new technologies also degrades the
                 data quality introducing linguistic values. This
                 research analyses the aforementioned problem and shows
                 that, in the field of lighting control systems, the
                 uncertainty in the measurements gathered from sensors
                 should be considered in the design of control loops. To
                 cope with this kind of problems Hybrid Intelligent
                 methods will be used. Moreover, a method for learning
                 equation-based white box models with this low quality
                 data is proposed. The equation-based models include a
                 representation of the uncertainty inherited in the
                 data. Two different evolutionary algorithms are use for
                 learning the models: the well-known NSGA-II genetic
                 algorithm and a multi-objective simulated annealing
                 algorithm hybridised with genetic operators. The
                 performance of both algorithms is found valid to evolve
                 this learning process. This novel approach is evaluated
                 with synthetic problems.",
}

Genetic Programming entries for Jose R Villar Alba Berzosa Enrique de la Cal Javier Sedano Marco Garcia-Tamargo

Citations