lacava / gecco_2019

Code to reproduce the paper "Semantic variation operators for multidimensional genetic programming"

Code to reproduce the experiments in the paper Semantic variation operators for multidimensional genetic programming

Experiments

Experiments can be run using analysis/ml-analyst/submit_jobs.py. See the command line options (python submit_jobs.py -h) for help.

As example, this command would launch the entire experiment:

python submit_jobs.py --r -ml FeatTuned,FeatSXOTuned,FeatRXOTuned,MLPmod,Linear,XGBoost -n_trials 10 -data ../penn-ml-benchmark/datasets/ -results results/

Notebooks

analysis/ contains these notebooks:

• results_tuning.ipynb produces the tuning results figures.
• results_benchmark.ipynb produces the comparisons to the Where are we now? paper.
• stats.ipynb depends on results_benchmark.ipynb and `results_tuning.ipynb', produces the statistical tests.
• results_benchmark-extended.ipynb contains code to reproduce the extended PMLB results.

Dependencies

• scikit-learn
• xgboost
• datasets come from PMLB
• FEAT