Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms
Neural and Evolutionary Computing
2017-05-01 v1
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.
Cite
@article{arxiv.1704.08774,
title = {Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms},
author = {Thomas Gabor and Lenz Belzner},
journal= {arXiv preprint arXiv:1704.08774},
year = {2017}
}
Comments
Measuring and Promoting Diversity in Evolutionary Algorithms @ GECCO 2017