Forest-genetic method to optimize parameter design of multiresponse experiment

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<title>Forest-genetic method to optimize parameter design of multiresponse experiment</title>
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<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20200022985">
<namePart>Carrión, Andrés </namePart>
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<namePart>Sozzi, Antonio </namePart>
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<abstract displayLabel="Summary">We propose a methodology for the improvement of the parameter design that consists of the combination of Random Forest (RF) with Genetic Algorithms (GA) in 3 phases: normalization, modelling and optimization. The first phase corresponds to the previous preparation of the data set by using normalization functions. In the second phase, we designed a modelling scheme adjusted to multiple quality characteristics and we have called it Multivariate Random Forest (MRF) for the determination of the objective function. Finally, in the third phase, we obtained the optimal combination of parameter levels with the integration of properties of our modeling scheme and desirability functions in the establishment of the corresponding GA. Two illustrative cases allow us to compare and validate the virtues of our methodology versus other proposals involving Artificial Neural Networks (ANN) and Simulated Annealing (SA).</abstract>
<note type="statement of responsibility">Adriana Villa-Murillo, Andrés Carrión, Antonio Sozzi</note>
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<topic>Inteligencia artificial</topic>
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<topic>Algoritmos</topic>
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<topic>Redes neuronales artificiales</topic>
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<topic>Genética</topic>
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<topic>Análisis multivariante</topic>
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<title>Revista Iberoamericana de Inteligencia Artificial</title>
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<publisher>IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</publisher>
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<identifier type="issn">1988-3064</identifier>
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<text>31/12/2020 Volumen 23 Número 66 - diciembre 2020 , p. 9-25</text>
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