Multigene genetic programming and its various applications

Niazkar, Majid

doi:10.1016/b978-0-12-821285-1.00019-1

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…Each individual in MGGP is a predictive model, which can consist of one or more than one gene (trees). Additionally, the fitness function is calculated for each MGGP individual to rank its performance in comparison to other individuals existing in a specific population 18 . If individuals created in the original population do not provide the desired fitness function, a new population is produced.…”

Section: Methodsmentioning

confidence: 99%

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables

Niazkar,

Moshari,

Khajavi

et al. 2024

Sci Rep

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Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020–2021. For each patient, the demographic characteristics, common laboratory tests at the time of admission, duration of hospitalization, admission to the intensive care unit (ICU), and mortality were collected through the electronic information system of the hospital. Then, the data were normalized and randomly divided into training and test data. Furthermore, mathematical prediction models were developed by MGGP for each gender. Finally, a sensitivity analysis was performed to determine the significance of input parameters on the COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the mortality of COVID-19 patients with an accuracy of 60–92%, the duration of hospital stay with an accuracy of 53–65%, and admission to the ICU with an accuracy of 76–91%, using common hematological tests at the time of admission. Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI techniques, such as MGGP, can be used in the triage and prognosis prediction of COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the estimation models, further studies on the role of the mentioned parameters in the pathophysiology of COVID-19 are recommended.

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Section: Methodsmentioning

confidence: 99%

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables

Niazkar,

Moshari,

Khajavi

et al. 2024

Sci Rep

Self Cite

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“…The primary difference between GEP and the Genetic Algorithm (GA) is related to the nature of each individual, so that individuals are linear rows of fixed length (chromosomes) in GA. Still, in gene expression programming, they are the same separate branches [40]. In GEP, the tree structure of the collections is emphasized, but the work of the genetic algorithm is based on a system of binary digits [41].…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran

Goodarzi

Niknam

Barzkar

et al. 2023

Water

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Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, Gene Expression Programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based on the WQI and FAHP-WQI methods, respectively. According to the results of the Wilcox chart, around 37.25% of the wells are in the C3S2 and C3S1 classes, which indicate poor water quality. Schoeller’s diagram placed the drinking water quality of the Yazd-Ardakan plain in acceptable, inadequate, and inappropriate categories. Afterwards, WQI, predicted by means of ML models, were compared on several statistical criteria. Finally, the comparative analysis revealed that MARS is slightly more accurate than the M5P model for estimating WQI.

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“…MGGP combines the model structure selection ability of standard genetic programming with the parameter estimation power of classical regression to capture nonlinear relationships between input and output variables [24][25]. It effectively searches for suitable relationships between input and output datasets, independent of the physical background of the data [26] and generates mathematical models of predictor response data that are "multi-gene" in nature, involving linear combinations of low-order non-linear transformations of input variables [25]. MGGP has been successfully applied to various engineering problems, such as behavioral modeling of structural engineering systems [24], identifying the dynamic prediction model for an overhead crane [27], and short-term load forecasting in power systems [28].…”

Section: Introductionmentioning

confidence: 99%

Process Optimization, Genetic Programming Modeling and Reliability Assessment of Bioactive Extracts Recovery From Phyllantus Emblica

Alanzi,

Alenezi,

Adeyi

et al. 2023

Preprint

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The feasibility of recovering bioactive antioxidants from Phyllantus emblica leaves using ethanol -water mixture (0 – 100%) and heat-assisted extraction technology (HAE-T) was investigated in this present study. To this effect, the process parameter of operating temperature (OT: 30 -50 oC), solid -to-liquid ratio (S:L: 1:20 - 1:60 g/mL) and extraction time (ET: 45 - 180 min) were investigated on the extract total phenolic content (TPC, mg GAE/g d.w), yield (EY, %) and antioxidant activity (μM AAE/g d.w) by employing the Box-Behnken experimental design (BBD) available in response surface methodology (RSM). Multi-objective process optimization using the desirability function algorithm to determine set of process variables that simultaneously optimize process responses of TPC, EY and AA was also investigated. The recovery process was thereafter modeled with BBD-RSM and multi gene genetic programming (MGGP) algorithm and model reliability assessment via Monte Carlo simulation were conducted for the best performing set of models for predicting TPC, EY and AA values. The HPLC characterization of the recovered extract was carried out to ascertain the phenolic compound constituents of the Phyllantus emblica leaf extract. The 50% ethanol solution was found to be the best for the optimal extraction of antioxidants from Phyllantus emblica leaves. The HAE-T was suitable for recovering bioactive extract from Phyllantus emblica leaves and investigated process parameters have impacts on the recovery process. The process parameters that simultaneously gave the optimum EY of 21.6565%, TPC of 67.116 mg GAE/g and AA of 3.68583 µM AAE/g were OT of 41.61 oC, S:L of 1:60 g/mL and ET of 180 min. The HPLC extract profiling revealed that the bioactive constituents present in the recovered extract were betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid. Both the developed BBD-RSM and evolved MGGP-based models predicted the observed process response of TPC, EY and AA satisfactorily with BBD-RSM models marginally better in their predictions. The reliability results showed that the BBD-RSM models predicted TPC, EY and AA values with high certainty of 99.985%, 97.569% and 98.661%, respectively.

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Multigene genetic programming and its various applications

Cited by 9 publications

References 40 publications

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables

Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran

Process Optimization, Genetic Programming Modeling and Reliability Assessment of Bioactive Extracts Recovery From Phyllantus Emblica

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