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The GA methodology can be further enhanced for designing other complex microfluidic systems.
GA methodology is applied to discharge and sediment load data obtained from Mississippi river at Missouri, St .Louis.
We demonstrate the power of the proposed GA methodology in designing multi-functional ladder devices that are extremely difficult to uncover using expensive experimental trial-and-error loops.
Genetic Algorithm (GA) methodology was used in the multiple objective optimization (using MATLAB R2010a software) to find the optimum cutting conditions for defect free drilling.
This research introduced integrated analytic network process (ANP) and genetic algorithm (GA) methodology to select the optimum mixture of RE material containing cement, expanded polystyrene EPS), and phase change materials PCM) with different moisture content.
History matching using GA methodology has been successfully validated for 2D synthetic reservoir.
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These results illustrate that the RBF-GA methodology could be a powerful technique during scale-up studies.
ANN-GA methodology was used to optimize the input space of the neural network model to find optimal settings for the laccase-mediated synthesis of iodine.
By applying the RBF-GA methodology, the efficiency of the laccase-mediated decolourization was improved by minimising the required amount of laccase and acetosyringone by 25%and21.7%7% respectively.
Such microorganisms are capable of consuming this gas, supporting the Microbial Prospection of Oil and Gas (MPOG) methodology.
The GA-MLHD methodology uses an initial random population of models (called chromosomes) and evolves from them highly accurate classifiers using a process that mimics natural selection.
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