This repository hosts the code for the EmbeddedHunter
algorithm for large-scale black-box optimization proposed in the Embedded Bandits for Large-Scale Black-Box Optimization paper, which is accepted at the thirty-first AAAI conference on artificial intelligence (AAAI-17). Poster can be found here.
Embedded Bandits for Large-Scale Black-Box Optimization.ipynb
: a notebook for a quick demonstration.run_aaai_demo.py
: a python script for running AAAI'17 paper experiments.Benchmark.py
: a python module for the large-scale problems used.Algorithms.py
: a python module implementingRESOO
,SRESOO
, andEmbeddedHunter
.*.pickle
: a collection of pickle files saving the outcome ofrun_aaai_demo.py
plot-table.tex
: a tex file to reproduce Figure 1 of the AAAI'17 paper.
To run all the experiments (it will take around 6 days), cd
to the directory and execute the following
>>python run_aaai_demo.py -1
Since all the *.pickle
files are there, the above command will just create a folder, which has all the experiments' results in a list of tabulated files. With these files at hand, you can now compile plot-table.tex
to create the pdf version of AAAI'17 paper's Figure 1.
If you write a scientific paper describing research that made use of this code, please consider citing the following paper:
@inproceedings{ash2017eh,
title={Embedded Bandits for Large-Scale Black-Box Optimization},
author={Abdullah Al-Dujaili and S. Suresh},
booktitle={Thirty-First AAAI Conference on Artificial Intelligence (AAAI'17},
year={2017}
}