<ul data-eligibleForWebStory="true"><li>The study explores the optimization of Genetic Algorithms with Multilayer Perceptron Networks for improving TinyFace recognition.</li><li>The empirical examination is conducted on MLP networks using three diverse datasets: TinyFace, Heart Disease, and Iris.</li><li>Three key methods are employed in the study: baseline training with default MLP settings, feature selection using Genetic Algorithm, and dimension reduction through Principal Component Analysis.</li><li>Results indicate that Genetic Algorithm consistently enhances accuracy in complex datasets by identifying critical features.</li><li>Principal Component Analysis is found beneficial for low-dimensional and noise-free datasets.</li><li>Comparison shows that feature selection and dimensionality reduction play interconnected roles in improving MLP performance.</li><li>The study contributes to the understanding of feature engineering and neural network parameter optimization, offering practical insights for various machine learning tasks.</li></ul>

Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition

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