Runtime phylogenetic analysis enables extreme subsampling for test-based problems

A phylogeny describes the evolutionary history of an evolving population. Evolutionary search algorithms can perfectly track the ancestry of candidate solutions, illuminating a population’s trajectory through the search space. However, phylogenetic analyses are typically limited to post-hoc studies of search performance. We introduce phylogeny-informed subsampling, a new class of subsampling methods that exploit runtime phylogenetic analyses for solving test-based problems. Specifically, we assess two phylogeny-informed subsampling methods – individualized random subsampling and ancestor-based subsampling – on three diagnostic problems and ten genetic programming (GP) problems from program synthesis benchmark suites. Overall, we found that phylogeny-informed subsampling methods enable problem-solving success at extreme subsampling levels where other subsampling methods fail. For example, phylogeny-informed subsampling methods more reliably solved program synthesis problems when evaluating just one training case per-individual, per-generation. However, at moderate subsampling levels, phylogeny-informed subsampling generally performed no better than random subsampling on GP problems. Our diagnostic experiments show that phylogeny-informed subsampling improves diversity maintenance relative to random subsampling, but its effects on a selection scheme’s capacity to rapidly exploit fitness gradients varied by selection scheme. Continued refinements of phylogeny-informed subsampling techniques offer a promising new direction for scaling up evolutionary systems to handle problems with many expensive-to-evaluate fitness criteria.

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Alexander Lalejini, Marcos Sanson, Jack Garbus, Matthew Andres Moreno, and Emily Dolson. 2024. Runtime phylogenetic analysis enables extreme subsampling for test-based problems. In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO ‘24). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3638530.3664090

• data 1 via Open Science Framework ❋
• data 2 via Open Science Framework ❋
• source code via Zenodo
• supplemental material