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Results of the GECCO'2016 3-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 7 participants in the field. The best function value per problem and participant (1000 times 7 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 DIKU, University of Copenhagen General UB-MO-CMA-ES UB-MO-CMA-ES generalisation for more than 2 objectives. No recombination of matrices and the largest contributor is selected instead of being chosen proportional to its contribution 956.985 2034
2 Simon Wessing Restarted local search + SMS-EMOA link 661.832 2552
3 Grays 244.802 3728
4 Mohammadamin Jahanpour and Bryan Tolson Pareto archived dynamically dimensioned search (PA-DDS) Convex Hull Contribution was used as selection metric for PA-DDS link link 185.18 3987
5 Artelys Artelys Knitro Artelys Knitro used in derivative-free mode with multistart link link 152.083 4920
6 Poly Montreal 141.33 4535
7 Al Jimenez Curved Trajectories Algorithm (CTA) email link 31.2688 6219

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.