# The application of Evolutionary and Nature Inspired Algorithms in Data Science and Data Analytics

arXiv: https://arxiv.org/abs/2202.03859 · HTML: https://mlsanity.com/paper/2202.03859/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 6 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization](https://mlsanity.com/paper/1302.6677/) (2013)
- [Search Algorithms for Automated Hyper-Parameter Tuning](https://mlsanity.com/paper/2104.14677/) (2021)
- [Embodied AI-Driven Operation of Smart Cities: A Concise Review](https://mlsanity.com/paper/2108.09823/) (2021)
- [Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics](https://mlsanity.com/paper/1908.08563/) (2019)
- [Evolutionary Computation, Optimization and Learning Algorithms for Data Science](https://mlsanity.com/paper/1908.08006/) (2019)
- [Data Analytics for Smart cities: Challenges and Promises](https://mlsanity.com/paper/2109.05581/) (2021)
- [HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization](https://mlsanity.com/paper/2109.05319/) (2021)

## Abstract (arXiv)

In the past 30 years, scientists have searched nature, including animals and insects, and biology in order to discover, understand, and model solutions for solving large-scale science challenges. The study of bionics reveals that how the biological structures, functions found in nature have improved our modern technologies. In this study, we present our discovery of evolutionary and nature-inspired algorithms applications in Data Science and Data Analytics in three main topics of pre-processing, supervised algorithms, and unsupervised algorithms. Among all applications, in this study, we aim to investigate four optimization algorithms that have been performed using the evolutionary and nature-inspired algorithms within data science and analytics. Feature selection optimization in pre-processing section, Hyper-parameter tuning optimization, and knowledge discovery optimization in supervised algorithms, and clustering optimization in the unsupervised algorithms.