Results of the GECCO'2016 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 14 participants in the field. The best function value per problem and participant (1000 times 14 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	Simon Wessing	Two-stage algorithms	Based on data from a preliminary experiment on test problems, a decision tree was built to decide which two-stage variant to apply on which dimension. The two-stage options were restarted local search and clustering method, the local search algorithms Nelder-Mead and CMA-ES. Before the two-stage algorithm starts, a singular L-BFGS-B run is tried.	link		677.607	5121
2	radka					641.278	4955
3	jarabas					637.327	5702
4	Al Jimenez	Curved Trajectories Algorithm (CTA)		email	link	602.851	6840
5	Artelys	Artelys Knitro	Artelys Knitro used in derivative-free mode with multistart	link	link	565.607	6576
6	Lepagnot	MSS	Hybrid Algorithm			522.657	5798
7	Brevilliers	BSA-DE-SA				460.125	6221
8	bujok					447.314	6184
9	Poly Montreal					378.073	7051
10	djagodzi					256.236	8418
11	Sumaiya Iqbal					230.281	8092
12	xd_zecia					125.728	12464
13	anonymous					48.7357	10636
14	anonymous					38.8972	10839

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.