Runtime phylogenetic analysis enables extreme subsampling for test-based problems
<|reference_start|>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.<|reference_end|>
@article{lalejini2024runtime,
    title={Runtime phylogenetic analysis enables extreme subsampling for test-based
  problems},
    author={Alexander Lalejini, Marcos Sanson, Jack Garbus, Matthew Andres Moreno,
  Emily Dolson},
    journal={arXiv preprint arXiv:2402.01610},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01610},
    primaryClass={cs.NE}
}