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genetic algorithm (ga) function in the matlab global optimization toolbox  (MathWorks Inc)


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    MathWorks Inc genetic algorithm (ga) function in the matlab global optimization toolbox
    Genetic Algorithm (Ga) Function In The Matlab Global Optimization Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/genetic+algorithm+(ga)+optimization+function/pm40263411-164-14-19
    Average 90 stars, based on 1 article reviews
    genetic algorithm (ga) function in the matlab global optimization toolbox - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Hyperinelasticity: An energy-based constitutive modelling approach to isothermal large inelastic deformation of polymers. Part I
    Article Snippet: The minimisation is performed via an in-house developed code in MATLAB®, using the genetic algorithm (GA) function.

    Article Title: Gene Regulatory Network Inference of Immunoresponsive Gene 1 ( IRG1 ) Identifies Interferon Regulatory Factor 1 ( IRF1 ) as Its Transcriptional Regulator in Mammalian Macrophages
    Article Snippet: This method was implemented in Matlab using the genetic algorithm (ga) function with the assumption that each cell phenotype represents a stable steady state (attractor) of the network.

    Article Title: A Model Based Approach for Monitoring Bordetella pertussis Fermentation with an Inline Spectro-Fluorescence Probe
    Article Snippet: The mechanistic model was calibrated using the genetic algorithm (ga) function in MATLAB.

    Article Title: A Generalized Gene-Regulatory Network Model of Stem Cell Differentiation for Predicting Lineage Specifiers
    Article Snippet: The network edge (interaction) pruning was performed using the modified version of the method proposed by (Crespo et al., 2013) re-implemented in MATLAB using the genetic algorithm (ga) function.

    Article Title: A multi-analytical approach to investigate DOM dynamics and alum dose control in enhanced coagulation process using wide-ranging surface waters
    Article Snippet: For each sample, the peak centre, peak width and peak height were adjusted using the genetic algorithm (GA) function [45] in MATLAB, and because of this, some differences in peak centres are expected among the different samples.

    Article Title: A computational strategy for predicting lineage specifiers in stem cell subpopulations.
    Article Snippet: Next, raw TRNs were contextualized (i.e., pruned) to the Booleanized gene expression profiles of each cell subpopulation using amethod previously developed in our group (Crespo et al., 2013), which was reimplemented and modified in MATLAB using a genetic algorithm (GA) function.

    Generated:

    Article Title: Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.
    Article Snippet: .. The models generated through RSM and ANN were further optimized by employing genetic algorithm ga function of MATLAB. .. The input parameters of ‘ ga ’ function were as follows: Population Type: 'double Vector'; Pop Init Range: [2x1 double]; population Size: 200; elite count: 2; crossover fraction: 1; migration direction: 'forward'; migration interval: 20; migration fraction: 0.2000; generations: 100; time limit: Inf; fitness limit:-Inf; stall gen limit: 50; stall time limit: 20; initial population: []; initial scores: []; plot interval: 1; creation fcn: @gacreationuniform; fitness scaling fcn: @fitscalingrank; selection fcn: @selectionstochunif; Crossover Fcn: @crossoverscattered; mutation fcn: {[1x1 function_handle] [ ] [ ]}; hybridfcn: []; display: 'off'; plotfcns: {[1x1 function_handle] [1x1 function_handle]};outputfcns: []; vectorized: 'off'.



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