Abstract
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 https://github.com/mohabedalgani/PGFLibPy.