# Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms

## TL;DR

The paper introduces genealogical diversity, a computationally efficient method to estimate relatedness between individuals in evolutionary algorithms by analyzing large, unused genome parts, aiding diversity measurement.

## Contribution

It presents a novel genealogical diversity measure based on genome analysis, improving diversity estimation in evolutionary algorithms.

## Key findings

- Genealogical diversity correlates with evolutionary process outcomes.
- The method efficiently estimates relatedness without extensive computation.
- It enhances diversity maintenance in evolutionary algorithms.

## Abstract

The evolutionary edit distance between two individuals in a population, i.e., the amount of applications of any genetic operator it would take the evolutionary process to generate one individual starting from the other, seems like a promising estimate for the diversity between said individuals. We introduce genealogical diversity, i.e., estimating two individuals' degree of relatedness by analyzing large, unused parts of their genome, as a computationally efficient method to approximate that measure for diversity.