Phylogenies provide direct accounts of the evolutionary trajectories behind evolved artifacts in genetic algorithm and artificial life systems. Phylogenetic analyses can also enable insight into evolutionary and ecological dynamics such as selection pressure and frequency-dependent selection. Traditionally, digital evolution systems have recorded data for phylogenetic analyses through perfect tracking where each birth event is recorded in a centralized data structure. This approach, however, does not easily scale to distributed computing environments where evolutionary individuals may migrate between a large number of disjoint processing elements. To provide for phylogenetic analyses in these environments, we propose an approach to enable phylogenies to be inferred via heritable genetic annotations rather than directly tracked. We introduce a “hereditary stratigraphy” algorithm that enables efficient, accurate phylogenetic reconstruction with tunable, explicit trade-offs between annotation memory footprint and reconstruction accuracy. In particular, we demonstrate an approach that enables estimation of the most recent common ancestor (MRCA) between two individuals with fixed relative accuracy irrespective of lineage depth while only requiring logarithmic annotation space complexity with respect to lineage depth. This approach can estimate, for example, MRCA generation of two genomes within 10% relative error with 95% confidence up to a depth of a trillion generations with genome annotations smaller than a kilobyte. We also simulate inference over known lineages, recovering up to 85.70% of the information contained in the original tree using 64-bit annotations.
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Matchmaker, matchmaker, make me a match: geometric, variational, and evolutionary implications of criteria for tag affinity
Genetic programming and artificial life systems commonly use tag matching to decide interactions between system components. However, the implications of criteria used to determine affinity between tags with respect evolutionary dynamics have not been directly studied. We investigate differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. In experiments, we find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming and artificial life systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature.
more » « less- NSF-PAR ID:
- 10403031
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Genetic Programming and Evolvable Machines
- Volume:
- 24
- Issue:
- 1
- ISSN:
- 1389-2576
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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