Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms
This work addresses diversity estimation for evolutionary algorithm practitioners, but it appears incremental as it builds on existing concepts of genealogical distance.
The paper tackles the problem of estimating diversity in evolutionary algorithms by proposing genealogical diversity, a method that analyzes unused genome parts to approximate evolutionary edit distance, resulting in a computationally efficient approach.
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.