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

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

@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",
• year = "2012",
• volume = "75",
• number = "1",
• pages = "219--225",
• month = jan,
• note = "Brazilian Symposium on Neural Networks (SBRN 2010) International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010)",
• keywords = "genetic algorithms, genetic programming, Low quality data, Multi-objective simulated annealing, Energy efficiency",
• ISSN = "0925-2312",
• DOI = "doi:10.1016/j.neucom.2011.02.025",
• URL = "http://www.sciencedirect.com/science/article/pii/S0925231211004115",
• size = "7 pages",
• 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 Antonio de la Cal Marin Javier Sedano Marco Antonio Garcia Tamargo

Citations