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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Notes
- 1.
Weighted edges can also be used, in which case the weights along the path should be summed. Here, we use unweighted edges.
- 2.
The correlation for tournament selection in the exploration diagnostic is incredibly high, however (1) the observed range of mean pairwise distance is so low that the correlation is almost certainly an artifact, and (2) this correlation is not observed for other fitness landscapes.
- 3.
In the pilot data set, we observed a strong positive correlation between phylogenetic diversity and fitness for lexicase selection. However, this correlation disappeared when we re-ran the experiments to generate the final data set.
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Acknowledgements
We thank members of the MSU ECODE lab, the MSU Digital Evolution lab, and the Cleveland Clinic Theory Division for the conversations that inspired this work. This research was supported by the National Science Foundation (NSF) through the BEACON Center (Cooperative Agreement DBI-0939454). Michigan State University provided computational resources through the Institute for Cyber-Enabled Research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF, UM, or MSU.
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Hernandez, J.G., Lalejini, A., Dolson, E. (2022). What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?. In: Banzhaf, W., Trujillo, L., Winkler, S., Worzel, B. (eds) Genetic Programming Theory and Practice XVIII. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-16-8113-4_4
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