KNN-Averaging for Noisy Multi-Objective Optimisation

(Supporting Submission for the QUATIC 2021 Paper for kNN-Averaging)

Authors
Stefan Klikovits and Paolo Arcaini;
National Institute of Informatics, Tokyo, Japan
Email: {lastname}@nii.ac.jp

Venue
14th International Conference on the Quality of Information and Communications Technology (QUATIC)
Quality in Cyber-physical Systems Track
September 8-11, 2021
Faro, Portugal Online

Thanks
The authors are supported by ERATO HASUO Metamathematics for Systems Design Project (No. JPMJER1603), JST. Funding Reference number: 10.13039/501100009024 ERATO. S. Klikovits is also supported by Grant-in-Aid for Research Activity Start-up 20K23334, JSPS.

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(Preliminary) Bibtex:

@InProceedings{KlikovitsA2021knnAveraging,
  author={Klikovits, Stefan and Arcaini, Paolo},
  title= {{KNN-Averaging for Noisy Multi-Objective Optimisation}},
  editor={Paiva, Ana C. R. and Cavalli, Ana Rosa and Ventura Martins, Paula and P{\'e}rez-Castillo, Ricardo},
  booktitle= {Proc. 14th Intl. Conf. on the Quality of Information and Communications Technology (QUATIC)},
  publisher= {Springer International Publishing},
  pages={503--518},
  series= {Communications in Computer and Information Science (CCIS)},
  volume={1439},
  isbn={978-3-030-85347-1},
  location= {Faro, Portugal (Online)},
  doi={10.1007/978-3-030-85347-1_36},
  year={2021}
}

In this repository

• KlikovitsArcaini-KNNAvgForNoisyNoisyMOO.pdf Preprint of the paper.
• Klikovits-Arcaini2021_KNNAvg_QUATIC_slides.pdf Slide deck used for the presentation at QUATIC.
• requirements.txt: Python dependencies. Install via pip install -r requirements.txt
• main.py: Entrypoint for search. To alter search settings, modify create_settings_and_run() function.
• knn_wrapper.py Wrapper for Pymoo benchmark problems. Use via knn_wrapper.wrap_problem(knn_wrapper.KNNAvgMixin, zdt.ZDT1, ...).
• output/ This will be where the search will put the output data (currently empty).
• result_plots/ Supporting data and result plots for the publication. Subfolders/filenames provide information about search settings
  • results_plots/KNNAvgMixin_ZDT1_V2/ ZDT1 benchmark with 2 search variables
  • results_plots/KNNAvgMixin_ZDT1_V4/ ZDT1 benchmark with 4 search variables
  • results_plots/KNNAvgMixin_ZDT1_V10/ ZDT1 benchmark with 10 search variables
  • results_plots/KNNAvgMixin_ZDT2_V2/ ZDT2 benchmark with 2 search variables
  • results_plots/KNNAvgMixin_ZDT2_V4/ ZDT2 benchmark with 4 search variables
  • results_plots/KNNAvgMixin_ZDT2_V10/ ZDT2 benchmark with 10 search variables
  • results_plots/KNNAvgMixin_ZDT3_V2/ ZDT3 benchmark with 2 search variables
  • results_plots/KNNAvgMixin_ZDT3_V4/ ZDT3 benchmark with 4 search variables
  • results_plots/KNNAvgMixin_ZDT3_V10/ ZDT3 benchmark with 10 search variables