Parameter Optimization Algorithms for Evolving Rule Models Applied to Freshwater Ecosystems

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@Article{Cao:2014:ieeeEC,
  author =       "Hongqing Cao and Friedrich Recknagel and 
                 Philip T. Orr",
  title =        "Parameter Optimization Algorithms for Evolving Rule
                 Models Applied to Freshwater Ecosystems",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2014",
  month =        dec,
  volume =       "18",
  number =       "6",
  pages =        "793--806",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 algorithm, cyanobacterial blooms, population-based
                 algorithms",
  DOI =          "doi:10.1109/TEVC.2013.2286404",
  ISSN =         "1089-778X",
  size =         "20 pages",
  abstract =     "Predictive rule models for early warning of
                 cyanobacterial blooms in freshwater ecosystems were
                 developed using a hybrid evolutionary algorithm (HEA).
                 The HEA has been designed to evolve IF-THEN-ELSE model
                 structures using genetic programming and to optimise
                 the stochastical constants contained in the model using
                 population-based algorithms. This paper intensively
                 investigated the performances of the following six
                 alternative population-based algorithms for parameter
                 optimisation (PO) of rule models within this hybrid
                 methodology: (1) Hill Climbing (HC), (2) Simulated
                 Annealing (SA), (3) Genetic Algorithm (GA), (4)
                 Differential Evolution (DE), (5) Covariance Matrix
                 Adaptation Evolution Strategy (CMA-ES), and (6)
                 Estimation of Distribution Algorithm (EDA). The
                 comparative study was carried out by predictive
                 modelling of chlorophyll-a concentrations and the
                 potentially toxic cyanobacterium Cylindrospermopsis
                 raciborskii cell concentrations based on water quality
                 time-series data in Lake Wivenhoe in Queensland
                 (Australia) from 1998 to 2009. The experimental results
                 demonstrate that with these PO methods, the rule models
                 discovered by the HEA proved to be both predictive and
                 explanatory whose IF condition indicates threshold
                 values for some crucial water quality parameters. When
                 Comparing different PO algorithms, HC always performed
                 best followed by DE, GA and EDA. Whilst CMA-ES
                 performed worst and the performance of SA varied with
                 different data sets.",
  notes =        "Also known as \cite{6637056}",
}

Genetic Programming entries for Hong-Qing Cao Friedrich Recknagel Philip T Orr

Citations