Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition

TL;DR

This paper empirically evaluates how genetic algorithms and PCA influence MLP performance across diverse datasets, highlighting the importance of feature selection and dimensionality reduction in neural network optimization.

Contribution

It introduces a comprehensive experimental framework comparing GA and PCA for MLP enhancement, providing practical insights for feature engineering in machine learning.

Findings

GA improves accuracy in complex datasets

PCA benefits low-dimensional, noise-free datasets

Feature selection and PCA have interdependent effects

Abstract

This study conducts an empirical examination of MLP networks investigated through a rigorous methodical experimentation process involving three diverse datasets: TinyFace, Heart Disease, and Iris. Study Overview: The study includes three key methods: a) a baseline training using the default settings for the Multi-Layer Perceptron (MLP), b) feature selection using Genetic Algorithm (GA) based refinement c) Principal Component Analysis (PCA) based dimension reduction. The results show important information on how such techniques affect performance. While PCA had showed benefits in low-dimensional and noise-free datasets GA consistently increased accuracy in complex datasets by accurately identifying critical features. Comparison reveals that feature selection and dimensionality reduction play interdependent roles in enhancing MLP performance. The study contributes to the literature on feature engineering and neural network parameter optimization, offering practical guidelines for a wide range of machine learning tasks