2013
DOI: 10.4236/jilsa.2013.51007
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Genetic Optimization of Artificial Neural Networks to Forecast Virioplankton Abundance from Cytometric Data

Abstract: Since viruses are able to influence the trophic status and community structure they should be accessed and accounted in ecosystem functioning and management models. So, this work met a set of biological, chemical and physical time series in order to explore the correlations with marine virioplankton community across different trophic gradients. The case studied is the Arraial do Cabo upwelling system, northeast of Rio de Janeiro State in Southeast coast of Brazil. The main goal is to evolve three type of artif… Show more

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Cited by 5 publications
(3 citation statements)
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“…Figure 1 shows a typical GA as described in [27]. The common problem-dependent constituents of genetic algorithm include the following: the encoding and the evaluation functions.…”
Section: Review Of Ga Representationsmentioning
confidence: 99%
“…Figure 1 shows a typical GA as described in [27]. The common problem-dependent constituents of genetic algorithm include the following: the encoding and the evaluation functions.…”
Section: Review Of Ga Representationsmentioning
confidence: 99%
“…For real time data acquisition the CytoSense flow cytometry (CytoBuoy bv, Worden, The Netherlands) was used with the same configurations of [15]. This device is connected to the computer by Wi-Fi connection and data transferred by the Internet for remote operation.…”
Section: Data Acquisition and In Situ Flow Cytometrymentioning
confidence: 99%
“…The CytoSense is equipped with a solid blue laser providing 20 mW at 488 nm, one frontal sensor named forward scatter (FWS) which measures the light deviation angle according to the passage of the particle through the laser, one side scatter (SWS, 446/500 nm) detector measuring the reflected light that has interacted with structures within the cells giving a sense of its granularity, and three others sensors to detect the red fluorescence produced by the amount of chlorophyll-a (FLR, 669/725 nm); one orange/yellow (FLO, 601/651) sensor and a green/yellow (FLY, 515/585 nm) sensor that measure the amount of phycocyanin and phycoerythrin fluorescences respectively [15].…”
Section: Data Acquisition and In Situ Flow Cytometrymentioning
confidence: 99%