Comparing SPO-tuned GP and NARX prediction models for stormwater tank fill level prediction

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

@InProceedings{Flasch:2010:cec,

• author = "Oliver Flasch and Thomas Bartz-Beielstein and Artur Davtyan and Patrick Koch and Wolfgang Konen and Tosin Daniel Oyetoyan and Michael Tamutan",
• title = "Comparing {SPO}-tuned {GP} and {NARX} prediction models for stormwater tank fill level prediction",
• booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
• year = "2010",
• address = "Barcelona, Spain",
• month = "18-23 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4244-6910-9",
• abstract = "The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e., Neural Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrisation. We compare different parameter tuning approaches, e.g. neuro-evolution and Sequential Parameter Optimization (SPO). In comparison to NN, GP yields superior results. By optimising GP parameters, GP runtime can be significantly reduced without degrading result quality. The SPO-based parameter tuning leads to results with significantly lower standard deviation as compared to the GA based parameter tuning. Our methodology can be transferred to other optimisation and simulation problems, where complex models have to be tuned.",
• DOI = "doi:10.1109/CEC.2010.5586172",
• notes = "WCCI 2010. Also known as \cite{5586172}",

}

Genetic Programming entries for Oliver Flasch Thomas Bartz-Beielstein Artur Davtyan Patrick Koch Wolfgang Konen Tosin Daniel Oyetoyan Michael Tamutan

Citations