# Soft Genetic Programming Binary Classifiers

arXiv: https://arxiv.org/abs/2101.08742 · HTML: https://mlsanity.com/paper/2101.08742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 21 Jan 2021.

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

- [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017)
- [DNA mixture deconvolution using an evolutionary algorithm with multiple populations, hill-climbing, and guided mutation](https://mlsanity.com/paper/2012.00513/) (2020)

## Abstract (arXiv)

The study of the classifier's design and it's usage is one of the most important machine learning areas. With the development of automatic machine learning methods, various approaches are used to build a robust classifier model. Due to some difficult implementation and customization complexity, genetic programming (GP) methods are not often used to construct classifiers. GP classifiers have several limitations and disadvantages. However, the concept of "soft" genetic programming (SGP) has been developed, which allows the logical operator tree to be more flexible and find dependencies in datasets, which gives promising results in most cases. This article discusses a method for constructing binary classifiers using the SGP technique. The test results are presented. Source code - https://github.com/survexman/sgp_classifier.