The Use of Genetic Algorithms in Response Surface Methodology

Alvarez, Manuel; Ilzarbe, Laura; Viles, Elisabeth; Tanco, Martín

doi:10.1080/16843703.2009.11673201

Cited by 37 publications

(17 citation statements)

References 1 publication

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“…The objective of using RSM is to optimize a response (output variable) which is influenced by a number of independent variables (input variables) [13, 14]. Consider the response y influenced by two independent variables x 1 and x 2 , which can be represented mathematically as follows:

\begin{matrix} y = f (x_{1}, x_{2}) + ɛ, \end{matrix}

where ɛ is the random error observed in the response y .…”

Section: Methods Of Predictionmentioning

confidence: 99%

Lifetime Estimation of the Upper Stage of GSAT-14 in Geostationary Transfer Orbit

David

Sharma

2014

International Scholarly Research Notices

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The combination of atmospheric drag and lunar and solar perturbations in addition to Earth's oblateness influences the orbital lifetime of an upper stage in geostationary transfer orbit (GTO). These high eccentric orbits undergo fluctuations in both perturbations and velocity and are very sensitive to the initial conditions. The main objective of this paper is to predict the reentry time of the upper stage of the Indian geosynchronous satellite launch vehicle, GSLV-D5, which inserted the satellite GSAT-14 into a GTO on January 05, 2014, with mean perigee and apogee altitudes of 170 km and 35975 km. Four intervals of near linear variation of the mean apogee altitude observed were used in predicting the orbital lifetime. For these four intervals, optimal values of the initial osculating eccentricity and ballistic coefficient for matching the mean apogee altitudes were estimated with the response surface methodology using a genetic algorithm. It was found that the orbital lifetime from these four time spans was between 144 and 148 days.

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\begin{matrix} y = f (x_{1}, x_{2}) + ɛ, \end{matrix}

where ɛ is the random error observed in the response y .…”

Section: Methods Of Predictionmentioning

confidence: 99%

Lifetime Estimation of the Upper Stage of GSAT-14 in Geostationary Transfer Orbit

David

Sharma

2014

International Scholarly Research Notices

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“…Park et al proposed a GA for constructing a cost‐constrained G ‐efficient design for a second‐order response surface model in cuboidal regions. Alvarez et al reviewed the previous literature on the use of GAs in response surface methodology. Chung et al showed a GA to construct optimal designs for mixture‐process variable experiments involving continuous and categorical noise variables that simultaneously optimize the scaled prediction variance ( SPV ) for the mean and the slope model.…”

Section: Algorithms For Construction Of D‐optimal Designsmentioning

confidence: 99%

Using a genetic algorithm to generate D‐optimal designs for mixture‐process variable experiments

Pradubsri

Chomtee

Borkowski

2019

Quality & Reliability Eng

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This article presents and develops a genetic algorithm (GA) to generate D‐efficient designs for mixture‐process variable experiments. It is assumed the levels of a process variable are controlled during the process. The GA approach searches design points from a set of possible points over a continuous region and works without having a finite user‐defined candidate set. We compare the performance of designs generated by the GA with designs generated by two exchange algorithms (DETMAX and k‐exchange) in terms of D‐efficiencies and fraction of design space (FDS) plots which are used to evaluate a design's prediction variance properties. To illustrate the methodology, examples involving three and four mixture components and one process variable are proposed for creating the optimal designs. The results show that GA designs have superior prediction variance properties in comparison with the DETMAX and k‐exchange algorithm designs when the design space is the simplex or is a highly‐constrained subspace of the simplex.

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“…Table presents more specialized applications such as designing microarray experiments, although there are some connections with those in Table . GAs have also been used to optimize response surface predictions from the designed experiments that Alvarez et al . reviews.…”

Section: Introductionmentioning

confidence: 99%

“…reviews. Alvarez et al . also lists some of the applications of GAs to designing experiments that we do but does not provide a comprehensive review of issues.…”

Section: Introductionmentioning

confidence: 99%

“…Table II presents more specialized applications such as designing microarray experiments, although there are some connections with those in Table I. GAs have also been used to optimize response surface predictions from the designed experiments that Alvarez et al 37 reviews. Alvarez et al 37 also lists some of the applications of GAs to designing experiments that we do but does not provide a comprehensive review of issues.…”

Section: Introductionmentioning

confidence: 99%

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Using Genetic Algorithms to Design Experiments: A Review

Lin

Anderson‐Cook

Hamada

et al. 2014

Quality & Reliability Eng

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Genetic algorithms (GAs) have been used in many disciplines to optimize solutions for a broad range of problems. In the last 20 years, the statistical literature has seen an increase in the use and study of this optimization algorithm for generating optimal designs in a diverse set of experimental settings. These efforts are due in part to an interest in implementing a novel methodology as well as the hope that careful application of elements of the GA framework to the unique aspects of a designed experiment problem might lead to an efficient means of finding improved or optimal designs. In this paper, we explore the merits of using this approach, some of the aspects of design that make it a unique application relative to other optimization scenarios, and discuss elements which should be considered for an effective implementation. We conclude that the current GA implementations can, but do not always, provide a competitive methodology to produce substantial gains over standard optimal design strategies. We consider both the probability of finding a globally optimal design as well as the computational efficiency of this approach. Copyright © 2014 John Wiley & Sons, Ltd.

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The Use of Genetic Algorithms in Response Surface Methodology

Cited by 37 publications

References 1 publication

Lifetime Estimation of the Upper Stage of GSAT-14 in Geostationary Transfer Orbit

Lifetime Estimation of the Upper Stage of GSAT-14 in Geostationary Transfer Orbit

Using a genetic algorithm to generate D‐optimal designs for mixture‐process variable experiments

Using Genetic Algorithms to Design Experiments: A Review

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