Understanding zooplankton long term variability through genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Marini:evobio12,
  author =       "Simone Marini and Alessandra Conversi",
  title =        "Understanding zooplankton long term variability
                 through genetic programming",
  booktitle =    "10th European Conference on Evolutionary Computation,
                 Machine Learning and Data Mining in Bioinformatics,
                 {EvoBIO 2012}",
  year =         "2012",
  month =        "11-13 " # apr,
  editor =       "Mario Giacobini and Leonardo Vanneschi and 
                 William S. Bush",
  series =       "LNCS",
  volume =       "7246",
  publisher =    "Springer Verlag",
  address =      "Malaga, Spain",
  pages =        "50--61",
  organisation = "EvoStar",
  isbn13 =       "978-3-642-29065-7",
  DOI =          "doi:10.1007/978-3-642-29066-4_5",
  keywords =     "genetic algorithms, genetic programming, Ecological
                 Modelling, Plankton Dynamics, Climate Change, Series",
  abstract =     "Zooplankton are considered good indicators for
                 understanding how oceans are affected by climate
                 change. While climate influence on zooplankton
                 abundance variability is currently accepted, its
                 mechanisms are not understood, and prediction is not
                 yet possible. We use Genetic Programming approach to
                 identify which environmental variables, and at which
                 extent, can be used to express zooplankton abundance
                 dynamics. The zooplankton copepod long term (since
                 1988) time series from the L4 station in the Western
                 English Channel, has been used as test case together
                 with local environmental parameters and large scale
                 climate indexes. The performed simulations identify a
                 set of relevant ecological drivers and highlight the
                 non linear dynamics of the Copepod variability. These
                 results indicate GP to be a promising approach for
                 understanding the long term variability of marine
                 populations.",
  notes =        "Plymouth, Devon.

                 Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held
                 in conjunction with EuroGP2012, EvoCOP2012,
                 EvoMusArt2012 and EvoApplications2012",
}

Genetic Programming entries for Simone Marini Alessandra Conversi

Citations