Results of the GECCO'2019 2-OBJ Track
"Raw" Result Data
On each problem participants were judged by the overall dominated hypervolume within the given budget of function evaluations. There were 3 participants in the field. The best function value per problem and participant (1000 times 3 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 | 476.885 | 1412 | ||||
| 2 | Artelys | 124.073 | 2258 | ||||
| 3 | Thomas Wortmann, Roger Ko, Chu Wy Ton | RBFMOpt | This method is a prototypical extension of the single-objective, model-based algorithm RBFOpt (https://github.com/coin-or/rbfopt). Similar to ParEGO, RBFMOpt repeatedly runs RBFOpt to optimize different weighted sums in order to approximate the Pareto front. The weights are drawn from a non-discrepancy sequence. In other words, the method uses conventional scalarizing, but, as a model-based algorithm, RBFOpt can benefit from and build on good solutions found in previous runs. |
92.1886 | 2330 |
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.
