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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Segond, M., Robilliard, D., & Fonlupt, C. (2006). Iterative filter generation using genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3905 LNCS, pp. 145–153). https://doi.org/10.1007/11729976_13
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