Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional de Colombia
2017
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| _version_ | 1876433570127413248 |
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| author | E. Canessa |
| author_facet | E. Canessa |
| contents | Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design E. Canessa S. Chaigneau Ingeniería Robust design parameter design pareto genetic algorithm response surface methodology 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. 2017 artículo científico 0120-5609 https://www.redalyc.org/articulo.oa?id=64352303012 en http://www.redalyc.org/revista.oa?id=643 Ingeniería e Investigación application/pdf Universidad Nacional de Colombia Ingeniería e Investigación (Colombia) Num.2 Vol.37 |
| format | Artículo científico |
| id | redalyc_64352303012 |
| institution | Redalyc |
| language | en |
| publishDate | 2017 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design E. Canessa Ingeniería Robust design parameter design pareto genetic algorithm response surface methodology Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design E. Canessa S. Chaigneau Ingeniería Robust design parameter design pareto genetic algorithm response surface methodology 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. 2017 artículo científico 0120-5609 https://www.redalyc.org/articulo.oa?id=64352303012 en http://www.redalyc.org/revista.oa?id=643 Ingeniería e Investigación application/pdf Universidad Nacional de Colombia Ingeniería e Investigación (Colombia) Num.2 Vol.37 |
| title | Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design |
| topic | Ingeniería Robust design parameter design pareto genetic algorithm response surface methodology |
| url | https://www.redalyc.org/articulo.oa?id=64352303012 |