A New Optimization Model for MLP Hyperparameter Tuning: Modeling and Resolution by Real-Coded Genetic Algorithm

Abstract

This paper introduces an efficient real-coded genetic algorithm (RCGA) evolved for constrained real-parameter optimization. This novel RCGA incorporates three specially crafted evolutionary operators: Tournament Selection (RS) with elitism, Simulated Binary Crossover (SBX), and Polynomial Mutation (PM). The application of this RCGA is directed toward optimizing the MLPRGA+5 model. This model is designed to configure Multilayer Perceptron neural networks by optimizing both their architecture and associated hyperparameters, including learning rates, activation functions, and regularization hyperparameters. The objective function employed is the widely recognized learning loss function, commonly used for training neural networks. The integration of this objective function is supported by the introduction of new variables representing MLP hyperparameter values. Additionally, a set of constraints is thoughtfully designed to align with the structure of the Multilayer Perceptron (MLP) and its corresponding hyperparameters. The practicality and effectiveness of the MLPRGA+5 approach are demonstrated through extensive experimentation applied to four datasets from the UCI machine learning repository. The results highlight the remarkable performance of MLPRGA+5, characterized by both complexity reduction and accuracy improvement.