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Results of the GECCO'2019 1-OBJ Track

"Raw" Result Data

On each problem participants were judged by the best (lowest) function value achieved within the given budget of function evaluations. There were 10 participants in the field. The best function value per problem and participant (1000 times 10 double precision numbers) is listed in this text file.

Participant Ranking

Participants were ranked based on aggregated problem-wise ranks (details here and here). The following results table lists participants with overall scores (higher is better) and the sum of ranks over all problems (lower is better) The table can be sorted w.r.t. these criteria.

rank participant method name method description software paper score  sum of ranks 
1 Nacim Belkhir AS-AC-CMA-ES Per Instance Algorithm Selection for low dimension and Per Instance Algorithm Configuration of CMA-ES based on problem features extracted from a uniform sample of the objective function. link 795.777 3345
2 avaneev biteopt2018 Plain BiteOpt on dimensions < 8, BiteOptDeep on dimensions >= 8. link link 651.518 3704
3 radka 649.493 3572
4 V-Stanovov LSHADE-RSP 525.917 4162
5 Artelys 443.563 4678
6 mini-mlog GAPSO A hybrid PSO+DE+Square function model+Polynomial function model with adapted behavior pool and adapted reset behaviors link 392.868 4523
7 Raphael Patrick Prager 166.69 8178
8 GERAD MADS Mesh Adaptive Direct Search algorithm
Implementation: NOMAD solver
Version: 4.0 (alpha, not available yet)
link link 70.8889 6888
9 coco 70.8249 8415
10 Jeremy PSO variant 18.4696 7717

Visualization of Performance Data

The following figure shows an aggregated view on the performance data.

The following figures show the same data, but separately for each problem dimension.