Systems modelling using genetic programming
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Cited by (44)
A review and perspective on hybrid modeling methodologies
2024, Digital Chemical EngineeringApplying genetic programming in estimation of frost layer thickness on horizontal and vertical plates at ultra-low temperature
2021, International Journal of RefrigerationCitation Excerpt :Related work was done by Kobayashi et al. (2011), who used GP for developing a feedback controller. Willis et al. (1997) used GP to model an input-output chemical process. Recently, Hosseini et al. (2020a, 2020b) and Moradkhani et al. (2020) developed general correlations for estimating the heat transfer coefficient and pressure drop in condensers using GP.
A Genetic Programming Approach for Construction of Surrogate Models
2019, Computer Aided Chemical EngineeringCitation Excerpt :Another (deterministic-based) approach to symbolic regression can be found in Cozad and Sahinidis (2018). In the context of Process Systems Engineering, GP has been applied to the obtention of dynamic models for a binary distillation column (Willis et al., 1997) and an extruder (Hinchliffe and Willis, 2003), process models of wastewater treatment reactors (Dürrenmatt and Gijer, 2012) and heat transfer correlations (Cai et al., 2006). Although the existence of these previous works, GP is not yet regarded as a tool commonly used for the generation of surrogate models.
An application of evolutionary system identification algorithm in modelling of energy production system
2018, Measurement: Journal of the International Measurement ConfederationCitation Excerpt :Models build on only the given input–output continuous data are known as regression models. The methods such as regression analysis, response surface methodology (RSM), partial least square regression, genetic programming (GP), artificial neural network (ANN), fuzzy logic (FL), M5- prime (M5′), support vector regression (SVR), adaptive neuro-fuzzy inference systems (ANFIS), etc. can be applied [2–7] to formulate these models. The models build must not only accurate predict the system output but shall also satisfy the system constraints.
Model development based on evolutionary framework for condition monitoring of a lathe machine
2015, Measurement: Journal of the International Measurement ConfederationCitation Excerpt :If there is a tie among the runs, the model of a given run with the lowest number of nodes (lower complexity) is chosen. The new evolutionary framework is implemented by modifying the software GPTIPS [29,30] code and developing its graphical interface (Fig. 7) for user friendly purpose. The parameter settings of MGGP and EN-MGGP are adjusted using a trial-and-error approach (Fig. 7) and based on the study conducted on applications of evolutionary algorithms in modelling the industrial processes [31–36].
Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming
2011, Applied Soft Computing Journal