# Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition

arXiv: https://arxiv.org/abs/2506.10184 · HTML: https://mlsanity.com/paper/2506.10184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 52 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://mlsanity.com/paper/2205.01491/) (2022)
- [How do Agile Software Startups deal with uncertainties by Covid-19 pandemic?](https://mlsanity.com/paper/2006.13715/) (2020)

## Built on by (most cited first, 1 of 1)

- [Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency](https://mlsanity.com/paper/2506.11508/) (2025)

## Abstract (arXiv)

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