https://github.com/amlalejini/phylogeny-informed-subsampling
https://github.com/amlalejini/phylogeny-informed-subsampling
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (8.6%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: amlalejini
- License: mit
- Language: Python
- Default Branch: main
- Size: 6.42 MB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Phylogeny-informed fitness estimation
Overview
Abstract
Phylogenies (ancestry trees) depict the evolutionary history of an evolving population. In evolutionary computing, a phylogeny can reveal how an evolutionary algorithm steers a population through a search space, illuminating the step-by-step process by which any solutions evolve. Thus far, phylogenetic analyses have primarily been applied as post-hoc analyses used to deepen our understanding of existing evolutionary algorithms. Here, we investigate whether phylogenetic analyses can be used at runtime to augment parent selection procedures during an evolutionary search. Specifically, we propose phylogeny-informed fitness estimation, which exploits a population's phylogeny to estimate fitness evaluations. We demonstrate phylogeny-informed fitness estimation in the context of the down-sampled lexicase and cohort lexicase selection algorithms on two diagnostic problems and four genetic programming (GP) problems. Our results indicate that phylogeny-informed fitness estimation can mitigate the drawbacks of down-sampled lexicase, improving diversity maintenance and search space exploration. However, the extent to which phylogeny-informed fitness estimation improves problem-solving success for GP varies by problem, subsampling method, and subsampling level. This work serves as an initial step toward improving evolutionary algorithms by exploiting runtime phylogenetic analysis.
Repository guide
docs/contains supplemental documentation for our methods.experiments/contains HPC job submission scripts, configuration files, and data analyses for all experiments.include/contains C++ implementations of experiment software (header only).scripts/contains generically useful scripts used in this work.source/contains .cpp files that can be compiled to run our experiments.
Owner
- Name: Alex Lalejini
- Login: amlalejini
- Kind: user
- Location: Grand Rapids, MI
- Company: Grand Valley State University
- Website: https://lalejini.com
- Twitter: amlalejini
- Repositories: 98
- Profile: https://github.com/amlalejini
Assistant Professor @ Grand Valley State University
GitHub Events
Total
Last Year
Issues and Pull Requests
Last synced: over 1 year ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- numpy ==1.24.3
- pandas ==2.0.1
- python-dateutil ==2.8.2
- pytz ==2023.3
- pyvarco ==1.0.0
- six ==1.16.0
- style ==1.1.0
- tzdata ==2023.3
- update ==0.0.1