This repository contains the source code for all the experiments in the following paper:
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The paper is accepted at the Genetic and Evolutionary Computation Conference (GECCO 2023).
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It is also accepted as a finalist for the Human-Competitive Awards (Humies 2023).
- All the codes for sweeping the parameter space are in the
parameter sweepfolder. RunplotAll.pyfor Figure 3 of the paper.
All the codes for the results in section 2.4 of the paper are in the exp1 - single nand folder.
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Run
evolveAfpo.pyto redo the experiments. -
Run
afpoPlots.pyandplotInOut.pyfor Figures 4, 5 and 6 of the paper. -
Code for the frequency sweep experiment from Figure 7 is in the
frequency sweepfolder.
- Run
plotHeatmap.pyfrom theexp2 - heatmapsfolder to recreate the heatmap in Figure 8.
Folder exp3 - polycomputation contains all the codes from section 4 of the paper.
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Run
evolveAfpo.pyto redo the experiments. -
Run
afpoPlots.pyandplotInOut.pyfor Figures 9 and 10 of the paper. -
Folder
exp4 - heatmapscontains codes to recreate Figure 11 showing the distribution of polycomputation.
- Run
robustness.pyto redo the experiment andplotRobustness.pyto make Figure 12.
evolveAfpo.pylaunches an evolutionary algorithm (Age-Fitness Pareto Optimization) with two or three objectives.constants.pycontains the algorithm's parameters such as population size and number of generations.switch_float.pyis the main simulator file. It simulators the 2D granular material taking as input the stiffness matrix and some gate parameters such as the positions of the ports, frequency of vibrations, and phase offset.
@article{parsa2023universal,
title={Universal Mechanical Polycomputation in Granular Matter},
author={Parsa, Atoosa and Witthaus, Sven and Pashine, Nidhi and O'Hern, Corey S and Kramer-Bottiglio, Rebecca and Bongard, Josh},
journal={arXiv preprint arXiv:2305.17872},
year={2023}
}
