Future Trends in Soft Computing Industrial Applications

Kordon, Arthur K.

doi:10.1109/fuzzy.2006.1681930

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…Unlike traditional regression analysis (in which the user must specify the model structure), GP automatically evolves both the structure and the parameters of the mathematical model (Searson (2009)). This symbolic regression can be used in academic (Alfaro-Cid et al ( 2009)) and industrial applications (Kordon (2006)).…”

Section: Meta-modelmentioning

confidence: 99%

Generic roughness meta-model in 3D printing by Fused Deposition Modeling

Asadollahi-Yazdi

Gardan²,

Lafon³

2021

Prog Addit Manuf

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Design for Additive Manufacturing (DfAM) aims at optimizing the product design based on design rules defined thanks to Additive Manufacturing (AM) constraints. To elaborate a functional design, several parameters like part orientation and layer thickness influence the results of mechanical strength, roughness, and manufacturing time. Surface roughness is an important input of Fused Deposition Modeling which impacts the product functionalities. To participate in product design optimization and DfAM development, this research suggests studying roughness models in order to clarify the best functional design methodology. This analysis permits to propose a meta-model that provides a better estimation for the surface roughness of the FDM products. A new roughness model is developed by a combination of genetic programming and symbolic regression due to the experimental data which helps the manufacturer to predict the surface quality of the products before fabrication. It enables us to estimate the surface roughness of all the AM products fabricated in a different value of layer thickness for all possible orientations in the space regarding part building direction.

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Section: Meta-modelmentioning

confidence: 99%

Generic roughness meta-model in 3D printing by Fused Deposition Modeling

Asadollahi-Yazdi

Gardan²,

Lafon³

2021

Prog Addit Manuf

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“…A GP can perform symbolic regression on raw data and variables that show nonlinear correlations [33] [34] [35] [36]. In this regard, GP automatically evolves both the parameters and the structure of the mathematical model.…”

Section: Regression Analysismentioning

confidence: 99%

Genetic programming-based regression for temporal data

Kuranga

Pillay

2021

Genet Program Evolvable Mach

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Various machine learning techniques exist to perform regression on temporal data with concept drift occurring. However, there are numerous nonstationary environments where these techniques may fail to either track or detect the changes. This study develops a genetic programming-based predictive model for temporal data with a numerical target that tracks changes in a dataset due to concept drift. When an environmental change is evident, the proposed algorithm reacts to the change by clustering the data and then induce nonlinear models that describe generated clusters. Nonlinear models become terminal nodes of genetic programming model trees. Experiments were carried out using seven nonstationary datasets and the obtained results suggest that the proposed model yields high adaptation rates and accuracy to several types of concept drifts. Future work will consider strengthening the adaptation to concept drift and the fast implementation of genetic programming on GPUs to provide fast learning for high-speed temporal data.

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“…In contrast to classical regression analysis, in which the user must specify the structure of the model, GP automatically evolves both the structure and the parameters of the mathematical model. Symbolic regression has had successful academic [3] and industrial applications [4].…”

Section: Introductionmentioning

confidence: 99%

Evolving Toxicity Models using MultigeneSymbolic Regression and Multiple Objectives

Hii¹,

Searson²,

Willis³

2011

IJMLC

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In this contribution a multi-objective genetic programming algorithm (MOGP) is used to perform symbolic regression. The genetic programming (GP) algorithm used is specifically designed to evolve mathematical models of predictor response data that are "multigene" in nature, i.e. linear combinations of low order non-linear transformations of the input variables. The MOGP algorithm simultaneously optimizes the dual (and competing) objectives of maximization of 'goodness-of-fit' to data and minimization of model complexity in order to develop parsimonious data based symbolic models. The functionality of the multigene MOGP algorithm is demonstrated by using it to generate an accurate, compact QSAR (quantitative structure activity relationship) model of existing toxicity data in order to predict the toxicity of chemical compounds.

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Future Trends in Soft Computing Industrial Applications

Cited by 12 publications

References 21 publications

Generic roughness meta-model in 3D printing by Fused Deposition Modeling

Generic roughness meta-model in 3D printing by Fused Deposition Modeling

Genetic programming-based regression for temporal data

Evolving Toxicity Models using MultigeneSymbolic Regression and Multiple Objectives

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