Results of the GECCO'2019 1-OBJ expensive 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 9 participants in the field. The best function value per problem and participant (1000 times 9 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 | Artelys | 934.769 | 2579 | ||||
| 2 | Nacim Belkhir | 732.135 | 3222 | ||||
| 3 | avaneev | biteopt2018 | link | link | 585.603 | 3361 | |
| 4 | mini-mlog | GAPSO | A hybrid PSO+DE+Square function model+Polynomial function model with adapted behavior pool and adapted reset behaviors | link | 381.063 | 4214 | |
| 5 | GERAD | MADS | Mesh Adaptive Direct Search algorithm Implementation: NOMAD solver Version: 4.0 (alpha, not available yet) | link | link | 272.365 | 4726 |
| 6 | V-Stanovov | LSHADE-RSP | 193.918 | 4972 | |||
| 7 | Jeremy | PSO variant | 97.7631 | 5921 | |||
| 8 | coco | sorry buggy code | 61.1242 | 7320 | |||
| 9 | Raphael Patrick Prager | 1.65026 | 8979 |
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.
