Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Machine-learning paradigms for selecting ecologically significant input variables
Engineering Applications of Artificial Intelligence
Environmental Modelling & Software
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
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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. This paper utilizes the 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 indices. 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.