# Kernel Density Estimation by Genetic Algorithm

arXiv: https://arxiv.org/abs/2203.01535 · HTML: https://mlsanity.com/paper/2203.01535/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [Kernel Density Estimation by Stagewise Algorithm with a Simple Dictionary](https://mlsanity.com/paper/2107.13430/) (2021)

## Abstract (arXiv)

This study proposes a data condensation method for multivariate kernel density estimation by genetic algorithm. First, our proposed algorithm generates multiple subsamples of a given size with replacement from the original sample. The subsamples and their constituting data points are regarded as itchromosome and itgene, respectively, in the terminology of genetic algorithm. Second, each pair of subsamples breeds two new subsamples, where each data point faces either itcrossover, itmutation, or itreproduction with a certain probability. The dominant subsamples in terms of fitness values are inherited by the next generation. This process is repeated generation by generation and brings the sparse representation of kernel density estimator in its completion. We confirmed from simulation studies that the resulting estimator can perform better than other well-known density estimators.