# Optimizing Feature Selection with Genetic Algorithms: A Review of Methods and Applications

arXiv: https://arxiv.org/abs/2409.14563 · HTML: https://mlsanity.com/paper/2409.14563/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 70 references that are in the map.
- First posted on arXiv 5 Sep 2024.

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

- [Review of Swarm Intelligence-based Feature Selection Methods](https://mlsanity.com/paper/2008.04103/) (2020)
- [Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey](https://mlsanity.com/paper/2405.03615/) (2024)

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

- [Temporal Property-driven Design Space Exploration with Reinforcement Learning for Cyber-Physical Systems](https://mlsanity.com/paper/2608.23440/) (2026)

## Abstract (arXiv)

Analyzing large datasets to select optimal features is one of the most important research areas in machine learning and data mining. This feature selection procedure involves dimensionality reduction which is crucial in enhancing the performance of the model, making it less complex. Recently, several types of attribute selection methods have been proposed that use different approaches to obtain representative subsets of the attributes. However, population-based evolutionary algorithms like Genetic Algorithms (GAs) have been proposed to provide remedies for these drawbacks by avoiding local optima and improving the selection process itself. This manuscript presents a sweeping review on GA-based feature selection techniques in applications and their effectiveness across different domains. This review was conducted using the PRISMA methodology; hence, the systematic identification, screening, and analysis of relevant literature were performed. Thus, our results hint that the field's hybrid GA methodologies including, but not limited to, GA-Wrapper feature selector and HGA-neural networks, have substantially improved their potential through the resolution of problems such as exploration of unnecessary search space, accuracy performance problems, and complexity. The conclusions of this paper would result in discussing the potential that GAs bear in feature selection and future research directions for their enhancement in applicability and performance.