Iterative filter generation using genetic programming

Authors:
Marc Segond;Denis Robilliard;Cyril Fonlupt
Affiliations:
Laboratoire d'Informatique du Littoral, Maison de la Recherche Blaise Pascal, CALAIS, France;Laboratoire d'Informatique du Littoral, Maison de la Recherche Blaise Pascal, CALAIS, France;Laboratoire d'Informatique du Littoral, Maison de la Recherche Blaise Pascal, CALAIS, France
Venue:
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Year:
2006

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Abstract

Oceanographers from the IFREMER institute have an hypothesis that the presence of so-called “retentive” meso-scale vortices in ocean and coastal waters could have an influence on watery fauna's demography. Up to now, identification of retentive hydro-dynamical structures on stream maps has been performed by experts using background knowledge about the area. We tackle this task with filters induced by Genetic Programming, a technique that has already been successfully used in pattern matching problems. To overcome specific difficulties associated with this problem, we introduce a refined scheme that iterates the filters classification phase while giving them access to a memory of their previous decisions. These iterative filters achieve superior results and are compared to a set of other methods.