Metodología de superficie de respuesta para estimar valores faltantes en un algoritmo genético de pareto usado en diseño de parámetros

Translated title of the contribution: Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design

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Abstract

We present an improved Pareto Genetic Algorithm (PGA), which finds solutions to problems of robust design in multi-response systems with 4 responses and as many as 10 control and 5 noise factors. Because some response values might not have been obtained in the robust design experiment and are needed in the search process, the PGA uses Response Surface Methodology (RSM) to estimate them. Not only the PGA delivered solutions that adequately adjusted the response means to their target values, and with low variability, but also found more Pareto efficient solutions than a previous version of the PGA. This improvement makes it easier to find solutions that meet the trade-off among variance reduction, mean adjustment and economic considerations. Furthermore, RSM allows estimating outputs’ means and variances in highly non-linear systems, making the new PGA appropriate for such systems.

Translated title of the contributionResponse surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design
Original languageSpanish
Pages (from-to)89-98
Number of pages10
JournalIngenieria e Investigacion
Volume37
Issue number2
DOIs
StatePublished - 2017

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