/ELL-GOMEA

Code repository for the article https://dl.acm.org/doi/abs/10.1145/3453141

Primary LanguageC++GNU General Public License v3.0GPL-3.0

ELL-GOMEA

Code repository for the article

"Arkadiy Dushatskiy, Tanja Alderliesten, and Peter A. N. Bosman. 2021. A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies. ACM Trans. Evol. Learn. Optim. 1, 2, Article 5 (June 2021), 23 pages. https://doi.org/10.1145/3453141"

To run Efficient Linkage Learning (ELL) algorithm:

  1. Compile GOMEA, ELL with using makefile_gomea, makefile_ell files
  2. Run GOMEA on the specific problem to get the baseline performance: python3 run_algorithms.py GOMEA [problem_index] [first_run] [last_run]
  3. Run ELL (the best performing algorithm) (or alternatively PLL, LARSLL): python3 run_algorithms.py [algorithm_name] [problem_index] [first_run] [last_run]
  4. Check out results in the corresponding folder with name results/[algorithm_name]/[problem_name]/[problem_size]/[run_index] The fitness of the elitist solution can be found in the elitistonly.dat file. The elitist solution itself can be found in the elitistsolution.dat.