# Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms

arXiv: https://arxiv.org/abs/1810.12470 · HTML: https://mlsanity.com/paper/1810.12470/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning](https://mlsanity.com/paper/2404.03359/) (2024)
- [Surrogate Fitness Metrics for Interpretable Reinforcement Learning](https://mlsanity.com/paper/2504.14645/) (2025)

## Abstract (arXiv)

Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum. In order to maintain diversity throughout the process of evolution, various means exist in literature. We analyze approaches to diversity that (a) have an explicit and quantifiable influence on fitness at the individual level and (b) require no (or very little) additional domain knowledge such as domain-specific distance functions. We also introduce the concept of genealogical diversity in a broader study. We show that employing these approaches can help evolutionary algorithms for global optimization in many cases.