EBADE

Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems

• This is an open-source code of EBADE implemented by Python 3.

• All codes are our originals.

How to run

1. Download all the files.

2. Open main.py with any code editor.

3. (If your environment does not have libraries written in requirements.txt, install them using the pip command.)

4. Run with/without debug, e.g., press F5 in the Microsoft Visual Studio Code editor.

Note: You can change the hyperparameter settings of EBADE in configuration.py.

Copyright

The copyright of the EBADE belongs to authors in the Evolutionary Intelligence Research Group (Nakata Lab) at Yokohama National University, Japan. You are free to use this code for research purposes. Please refer to the following article;

Kei Nishihara and Masaya Nakata, "Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems," Complex Intell. Syst., vol. 10, no. 3, pp. 3633–3656, Jun. 2024.

@article{nishihara2024emulation,
  title     = "{Emulation-based adaptive differential evolution: fast and
               auto-tunable approach for moderately expensive optimization
               problems}",
  author    = "Nishihara, Kei and Nakata, Masaya",
  journal   = "Complex Intell. Syst.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  10,
  number    =  3,
  pages     = "3633--3656",
  month     =  jun,
  year      =  2024,
  doi       = "10.1007/s40747-023-01340-9",
  issn      = "2198-6053,2199-4536",
  language  = "en"
}