What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?

• URL: http://arxiv.org/abs/2108.12586v1
• Date: Sat, 28 Aug 2021 06:49:14 GMT
• Title: What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?
• Authors: Jose Guadalupe Hernandez, Alexander Lalejini, Emily Dolson
• Abstract summary: Phylogenetic diversity metrics are a class of metrics popularly used in biology. We find that, in most cases, phylogenetic metrics behave meaningfully differently from other diversity metrics.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is generally accepted that "diversity" is associated with success in evolutionary algorithms. However, diversity is a broad concept that can be measured and defined in a multitude of ways. To date, most evolutionary computation research has measured diversity using the richness and/or evenness of a particular genotypic or phenotypic property. While these metrics are informative, we hypothesize that other diversity metrics are more strongly predictive of success. Phylogenetic diversity metrics are a class of metrics popularly used in biology, which take into account the evolutionary history of a population. Here, we investigate the extent to which 1) these metrics provide different information than those traditionally used in evolutionary computation, and 2) these metrics better predict the long-term success of a run of evolutionary computation. We find that, in most cases, phylogenetic metrics behave meaningfully differently from other diversity metrics. Moreover, our results suggest that phylogenetic diversity is indeed a better predictor of success.

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