JACIII Vol.26 No.2 pp. 169-177
doi: 10.20965/jaciii.2022.p0169


PGFLibPy: An Open-Source Parallel Python Toolbox for Genetic Folding Algorithm

Mohammad A. Mezher

Fahd Bin Sultan University
Jordan Street, Tabuk 15700, Saudi Arabia

June 17, 2020
January 11, 2022
March 20, 2022
parallel computational intelligence, genetic folding, evolutionary algorithms, classification, kernels tricks
PGFLibPy: An Open-Source Parallel Python Toolbox for Genetic Folding Algorithm

PGFLibPy in the visual studio code environment

Genetic folding (GF) is a robust evolutionary optimization algorithm. For efficient hyper-scale GFs, a hybrid parallel approach based on CPU architecture Parallel GF (PGF) is proposed. It aids in resolving kernel tricks that are difficult to predict using conventional optimization approaches. The regression and classification problems are solved using PGF. Four concurrent CPUs are formed to parallelize the GF, and each executes eight threads. It is also easily scalable to multi-core CPUs. PGFLibPy is a Python-based machine learning framework for classification and regression problems. PGFLibPy was used to build a model of the UCI dataset that reliably predicts regression values. The toolbox activity is used for binary and multiclassification datasets to classify UCI. PGFLibPy’s has 25 Python files and 18 datasets. Dask parallel implementation is being considered in the toolbox. According to this study, this toolbox can categorize and predict models on any other dataset. The source code, binaries, and dataset are available for download at

Cite this article as:
Mohammad A. Mezher, “PGFLibPy: An Open-Source Parallel Python Toolbox for Genetic Folding Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.2, pp. 169-177, 2022.
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Last updated on May. 20, 2022