Genetic programming automated machine learning: architecture and performance evaluation

Genetic programming-based automated machine learning (GP-AutoML) automates machine-learning pipelines through evolutionary search, but its implementation requires programming knowledge and configuration of multiple software components. This study presents a web-based platform that integrates the tree-based pipeline optimization tool (TPOT) to support GP-AutoML through a graphical modeling workflow. The platform incorporates conventional machinelearning modules, GP-AutoML configuration, result visualization, user authentication, and cloud-based storage, supported by a flask backend, scikit-learn, TPOT, and Firebase services. A comparative evaluation was conducted between the proposed web-based platform and Google colab Python scripting using five binary-classification datasets with varying numbers of records, features, and class distributions. Testing accuracy, Class 1 precision, recall, F1- score, and time-to-completion were evaluated under predefined GP parameter settings. Google colab obtained higher testing accuracy for all datasets. However, the differences were small, ranging from 0.01 to 0.06. This indicates comparable predictive performance by the web-based GUI platform. For the moderately balanced dataset, both environments achieved stronger Class 1 precision, recall, and F1-score. Although the web-based platform required a longer time-to-completion for every dataset, the evaluated runs remained practical for rapid model development. The findings demonstrate the feasibility of delivering TPOT-based GP-AutoML through an integrated web-based architecture that supports accessible visual modeling without direct programming or local software installation.