Macro-grammatical evolution for nonlinear time series modeling-a case study of reservoir inflow forecasting

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@Article{journals/ewc/Chen11,
  author =       "Li Chen",
  title =        "Macro-grammatical evolution for nonlinear time series
                 modeling-a case study of reservoir inflow forecasting",
  journal =      "Engineering with Computers",
  year =         "2011",
  volume =       "27",
  number =       "4",
  pages =        "393--404",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, streamflow forecasting, nonlinear model,
                 macroevolutionary algorithm",
  ISSN =         "0177-0667",
  DOI =          "doi:10.1007/s00366-011-0212-3",
  size =         "12 pages",
  abstract =     "Streamflow forecasting is significantly important for
                 planning and operating water resource systems. However,
                 stream flow formation is a highly nonlinear, time
                 varying, spatially distributed process and difficult to
                 forecast. This paper proposes a nonlinear model which
                 incorporates improved real-coded grammatical evolution
                 (GE) with a genetic algorithm (GA) to predict the
                 ten-day inflow of the De-Chi Reservoir in central
                 Taiwan. The GE is a recently developed
                 evolutionary-programming algorithm used to express
                 complex relationships among long-term nonlinear time
                 series. The algorithm discovers significant input
                 variables and combines them to form mathematical
                 equations automatically. Using GA with GE optimises an
                 appropriate type of function and its associated
                 coefficients. To enhance searching efficiency and
                 genetic diversity during GA optimisation, the
                 macro-evolutionary algorithm (MA) is processed as a
                 selection operator. The results using an example of
                 theoretical nonlinear time series problems indicate
                 that the proposed GEMA yields an efficient optimal
                 solution. GEMA has the advantages of its ability to
                 learn relationships hidden in data and express them
                 automatically in a mathematical manner. When applied to
                 a real world case study, the fittest equation generated
                 through GEMA used only a single input variable in a
                 reasonable nonlinear form. The predicting accuracies of
                 GEMA were better than those of the traditional linear
                 regression (LR) model and as good as those of the
                 back-propagation neural network (BPNN). In addition,
                 the predicting of ten-day reservoir inflows reveals the
                 effectives of GEMA, and standardisation is beneficial
                 to model for seasonal time series.",
  affiliation =  "Department of Civil Engineering and Engineering
                 Informatics, Chung Hua University, Hsin Chu, 30012
                 Taiwan, R.O.C",
  bibdate =      "2011-09-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ewc/ewc27.html#Chen11",
  URL =          "http://dx.doi.org/10.1007/s00366-011-0212-3",
}

Genetic Programming entries for Li Chen

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