https://github.com/amlalejini/gptp-2018-exploring-genetic-programming-systems-with-map-elites
Repository to contain material related to our 2018 GPTP contribution.
https://github.com/amlalejini/gptp-2018-exploring-genetic-programming-systems-with-map-elites
Science Score: 36.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: zenodo.org -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.7%) to scientific vocabulary
Repository
Repository to contain material related to our 2018 GPTP contribution.
Basic Info
- Host: GitHub
- Owner: amlalejini
- License: mit
- Language: JavaScript
- Default Branch: master
- Homepage: http://lalejini.com/GPTP-2018-Exploring-Genetic-Programming-Systems-with-MAP-Elites/
- Size: 13.9 MB
Statistics
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 5
- Releases: 1
Metadata Files
README.md
Navigation
Overview
This repository is associated with our 2018 Genetic Programming Theory and Practice (GPTP) workshop contribution, citation pending.
Repository guide
- analysis/
- This directory contains our fully detailed statistical analyses as well as the code necessary to reproduce publication figures.
- data/
- This directory contains the experiment data used by our analyses.
- experiment/
- This directory contains the source code (C++) for all experiments as well as a few utility scripts (Python).
Dependencies
This code depends on Empirical. These specific experiments were run using the version in this commit.
Contribution Authors
Abstract
MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations to fitness allows the user to develop a better understanding of how each trait relates to fitness and how they interact with each other. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provide do allow the users to better understand the underlying problem. Such insights extend into the underlying structure of the problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We further discuss how this approach can promote more complex and efficient solutions.
Computational Substrate
In this work, we evolved simple linear genetic programs. See the publication for a fully detailed description of the linear GP representation used.
Details about the instruction set used in our experiments can be found here.
Data and Analyses
The data used in our analyses can be found in this repository: ./data/
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
Committers
Last synced: 12 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Alex Lalejini | a****i@g****m | 86 |
| Emily Dolson | E****n@g****m | 20 |
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 5
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 1
- Total pull request authors: 0
- Average comments per issue: 0.0
- 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
- amlalejini (5)