Multiple response optimization: Analysis of genetic programming for symbolic regression and assessment of desirability functions

Gomes, Fabrício Maciel; Pereira, Fabiana de Souza; Silva, Aneirson Francisco da; Silva, Messias Borges da

doi:10.1016/j.knosys.2019.05.002

Cited by 29 publications

(23 citation statements)

References 28 publications

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“…In this section, it is shown the features of two techniques that were used in the proposed procedure to deal with problems with multiple responses by the Response Surface Methodology (RMS), considering the occurrence of uncertainties in the factors of the studied process. As highlighted by Gomes et al (2019)), in process optimization problems, it is of fundamental importance to develop adequate models that mathematically describe the relationship between independent variables x i and response variables y s . The models can be classified in two broad classes (Chen et al, 2018): phenomenological models and empirical models.…”

Section: Background Of Response Surface Methodology and Optimization Via Monte Carlo Simulationmentioning

confidence: 99%

Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty

Silva

Marins

Oliveira

et al. 2021

Int J Adv Manuf Technol

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The Response Surface Methodology (RSM), which uses a quadratic empirical function as an approximation to the original function and allows the identification of relationships between independent variables x i and dependent variables y s associated with multiple responses, stands out. The main contribution of the present study is to propose an innovative procedure for the optimization of experimental problems with multiple responses, which considers the insertion of uncertainties in the coefficients of the obtained empirical functions in order to adequately represent real situations. This new procedure, which combines RSM with the Finite Elements (FE) method and the Monte Carlo Simulation Optimization (OvMCS), was applied to a real stamping process of a Brazilian multinational automotive company. For RSM with multiple responses, were compared the results obtained using the agglutination methods: Compromise Programming, Desirability Function (DF), and the Modified Desirability Function (MDF). The functions were optimized by applying the Generalized Reduced Gradient (GRG) algorithm, which is a classic procedure widely adopted in this type of experimental problem, without the uncertainty in the coefficients of independent factors. The advantages offered by this innovative procedure are presented and discussed, as well as the statistical validation of its results. It can be highlighted, for example, that the proposed procedure reduces, and sometimes eliminates, the need for additional confirmation experiments, as well as a better adjustment of factor values and response variable values when comparing to the results of RSM with classic multiple responses. The new proposed procedure added relevant and useful information to the managers responsible for the studied stamping process. Moreover, the proposed procedure facilitates the improvement of the process, with lower associated costs.

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Section: Background Of Response Surface Methodology and Optimization Via Monte Carlo Simulationmentioning

confidence: 99%

Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty

Silva

Marins

Oliveira

et al. 2021

Int J Adv Manuf Technol

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show abstract

“…A symbolic regression was performed by the genetic programming using Eureqa Formulize v. 1.2.4.0 software (Nutonian, Inc., Boston, MA, USA) [23]. Four fit models were selected according to the maximum coefficient of determination (R 2 ≥ 0.99).…”

Section: Central Composite Rotational Design (Ccrd): Interplay Of Carbon and Nitrogen Sourcesmentioning

confidence: 99%

“…Likewise, the models represent more than 99.8% of the variation (R 2 = 0.998), indicating an excellent fit quality to experimental data. Similarly, Gomes et al [23] built optimization models that exhibited minimal relative error using genetic programming by Eureqa. The optimization of the models considered the yields through the NMinimize function of Wolfram Mathematica because of the exceeding cost of commercial cellulase used in enzymatic hydrolysis [17].…”

Section: Carbon and Nitrogen Optimization Through Ccrd And Genetic Programmingmentioning

confidence: 99%

Comparative Highly Efficient Production of β-glucan by Lasiodiplodia theobromae CCT 3966 and Its Multiscale Characterization

et al. 2021

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Lasiodiplodan, a (1→6)-β-d-glucan, is an exopolysaccharide with high commercial value and many applications in food, pharmaceuticals, and cosmetics. Current industrial production of β-glucans from crops is mostly by chemical routes generating hazardous and toxic waste. Therefore, alternative sustainable and eco-friendly pathways are highly desirable. Here, we have studied the lasiodiplodan production from sugarcane bagasse (SCB), a major lignocellulosic agricultural residue, by Lasiodiplodia theobromae CCT 3966. Lasiodiplodan accumulated on SCB hydrolysate (carbon source) supplemented with soybean bran or rice bran (nitrogen source) was 16.2 [6.8 × 103 Da] and 22.0 [7.6 × 103 Da] g/L, respectively. Lasiodiplodan showed high purity, low solubility, pseudoplastic behavior and was composed of glucose units. Moreover, the exopolysaccharides were substantially amorphous with moderate thermal stability and similar degradation temperatures. To our knowledge, this is the first report on the highest production of SCB-based lasiodiplodan to date. L. theobromae, as a microbial cell factory, demonstrated the commercial potential for the sustainable production of lasiodiplodan from renewable biomass feedstock.

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“…Desirability optimization methodology, introduced by Derringer and Suich, is a promising choice for the simultaneous optimization of various functions of a process. In this methodology, responses are converted into dimensionless desirability values called d i , in which desirability values close to zero indicates an undesirable quality, whereas values close to unity signal more desirable regions [30,31]. The global desirability of the process can be obtained using Eq.…”

Section: Desirability Functionmentioning

confidence: 99%

Modeling and optimization of simultaneous degradation of rhodamine B and acid red 14 binary solution by homogeneous Fenton reaction: a chemometrics approach

Aliasgharlou¹,

Bahram²,

Zolfaghari³

et al. 2020

Turk J Chem

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This study aimed to propose a mathematical method to investigate and optimize the simultaneous elimination process of multiple organic pollutants using the Fenton process. Hence, the treatment of rhodamine B (RB) and acid red 14 (AR14) dyes in their binary solution was studied. Multivariate curve resolution alternating least square (MCR-ALS), a novel chemometric method, was applied along with correlation constraints to resolute the UV-Vis spectrophotometric data, enabling quantification of investigated dyes despite a high spectral overlapping. Response surface methodology was adopted to assess the model and optimize individual and interactive effects of three independent factors (Fe 2+ , H 2 O 2 and initial pH) on the simultaneous elimination of RB and AR14. The values of the regression coefficient for RB and AR14 were determined as 98.48 and 98.67 percent, respectively, revealing the reliability of the obtained polynomial models to predict decolorization efficiencies. Desirability function was employed to optimize the independent variables to attain the highest possible degradation performance for both dyes in their binary solution. At the optimum point of operation ([Fe 2+ ] = 143.88 mg/L, [H 2 O 2 ] = 126.89 mg/L and pH = 3.71), degradation efficiencies of RB and AR14 were found as 81.58% and 80.22%, respectively, which were nearly identical to the experimental results.

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Multiple response optimization: Analysis of genetic programming for symbolic regression and assessment of desirability functions

Cited by 29 publications

References 28 publications

Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty

Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty

Comparative Highly Efficient Production of β-glucan by Lasiodiplodia theobromae CCT 3966 and Its Multiscale Characterization

Modeling and optimization of simultaneous degradation of rhodamine B and acid red 14 binary solution by homogeneous Fenton reaction: a chemometrics approach

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