Raw data (e.g. raw experimental results) can be found here: https://doi.org/10.4121/19314218.
Processing-* contain the relevant notebooks for generating the graphs from data, requires numpy, pandas, an rpy2 installation using an R installation which has dplyr & ggplot2 installed.
If you use this work, please cite the relevant work.
- Submitted to GECCO 2022
- Published at GECCO 2022: https://dl.acm.org/doi/10.1145/3512290.3528828
- ArXiv: https://arxiv.org/abs/2203.05970 - prefer citing the published version instead.
source-singleobjective also includes a modified version of DSMGA-II that uses the Interleaved Multistart Scheme. As per https://github.com/tianliyu/DSMGA-II, if used within a publication, please include the following citation: S.-H. Hsu and T.-L. Yu, Optimization by pairwise linkage detection, incremental linkage set, and restricted/back mixing: DSMGA-II, Genetic and Evolutionary Computation Conference (GECCO 2015).
DAEDALUS – Distributed and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability
This research code was developed as part of the research programme Open Technology Programme with project number 18373, which was financed by the Dutch Research Council (NWO), Elekta, and Ortec Logiqcare.
Project leaders: Peter A.N. Bosman, Tanja Alderliesten Researchers: Alex Chebykin, Arthur Guijt, Vangelis Kostoulas Main code developer: Arthur Guijt