We lifted 11 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| adaptive-intelligent-robotics/dominated-novelty-search | canonical | 8 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| customize_axis | Ran | adaptive-intelligent-robotics/dominated-novelty-search/analysis/kheperax_plot.py code served (permissive licence) · get_code("8481ec92f78f7ef9") |
| get_config | Ran | adaptive-intelligent-robotics/dominated-novelty-search/utils.py code served (permissive licence) · get_code("a8a13ebef20b5baf") |
| get_latest_run_for_env_and_pop | Ran | adaptive-intelligent-robotics/dominated-novelty-search/analysis/plot_scatter.py code served (permissive licence) · get_code("80c7020f5674e152") |
| get_metrics | Ran | adaptive-intelligent-robotics/dominated-novelty-search/utils.py code served (permissive licence) · get_code("895137189de3e4c2") |
| plot_kheperax_maze | Ran | adaptive-intelligent-robotics/dominated-novelty-search/scatter_plots.py code served (permissive licence) · get_code("e6234ac401c7184b") |
| plot_kheperax_maze_arrows | Ran | adaptive-intelligent-robotics/dominated-novelty-search/scatter_plots.py code served (permissive licence) · get_code("19a35aabdb3929a9") |
| pretty_env_name | Ran | adaptive-intelligent-robotics/dominated-novelty-search/analysis/kheperax_plot.py code served (permissive licence) · get_code("6c79c29340ddec69") |
| pretty_env_name | Ran | adaptive-intelligent-robotics/dominated-novelty-search/analysis/plot_main.py code served (permissive licence) · get_code("c80cb839cb866e91") |
| get_latest_run_for_env | Not yet run | adaptive-intelligent-robotics/dominated-novelty-search/analysis/plot_scatter.py code served (permissive licence) · get_code("2d52368b785ffc79") |
| load_data | Not yet run | adaptive-intelligent-robotics/dominated-novelty-search/analysis/kheperax_plot.py code served (permissive licence) · get_code("6f9be66b0fdc3295") |
| load_data | Not yet run | adaptive-intelligent-robotics/dominated-novelty-search/analysis/plot_main.py code served (permissive licence) · get_code("99b6de3648e49549") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution. While research has focused on improving specific aspects of Quality-Diversity algorithms, surprisingly little attention has been paid to investigating alternative formulations of local competition itself -- the core mechanism distinguishing Quality-Diversity from traditional evolutionary algorithms. Most approaches implement local competition through explicit collection mechanisms like fixed grids or unstructured archives, imposing artificial constraints that require predefined bounds or hard-to-tune parameters. We show that Quality-Diversity methods can be reformulated as Genetic Algorithms where local competition occurs through fitness transformations rather than explicit collection mechanisms. Building on this insight, we introduce Dominated Novelty Search, a Quality-Diversity algorithm that implements local competition through dynamic fitness transformations, eliminating the need for predefined bounds or parameters. Our experiments show that Dominated Novelty Search significantly outperforms existing approaches across standard Quality-Diversity benchmarks, while maintaining its advantage in challenging scenarios like high-dimensional and unsupervised spaces.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.00593")
get_code_for_paper("2502.00593")
have("2502.00593")
Connect an agent — have() is free.