Results of the GECCO'2017 1OBJ 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 13 participants in the field. The best function value per problem and participant (1000 times 13 double precision numbers) is listed in this text file.
Participant Ranking
Participants were ranked based on aggregated problemwise 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  LB  DTSCMAES or BOBYQA  Doubly trained surrogate CMAES (DTSCMAES) with 5% of the number of originalevaluated points per generation, or Powell's BOBYQA (Dlib C++ reimplementation) if evaluations budget is less than 15D.  824.715  4733  
2  Simon Wessing  RLMBO+LBFGSB+RS  Restarted local modelbased optimization (alpha version), LBFGSB, and random search  link  691.064  5562  
3  Artelys  Artelys Knitro  Artelys Knitro used in derivativefree mode with multistart  link  link  593.055  6482  
4  Al Jimenez  575.891  5386  
5  bujok  IDEbdQ 

496.176  5728  
6  SACOBRA  SACOBRA  Selfadjusting Constrained Optimization with RBF and online model selection. (Due to time shortage and computational time limits only less than 85% of the problems were addressed completely)  468.327  5884  
7  EMAGIN's Tomcat  Emagin's Tomcat (Sparrow mode)  Developed by Mohammadamin Jahanpour  433.147  6218  
8  Giacomo Nannicini  333.639  6496  
9  Poly Montreal  208.556  7857  
10  Jeremy M  Custom algorithm  198.68  8602  
11  Ralf S.  Mix of PSO and GA  170.237  8045  
12  djagodzi  DES  Differential Evolution Strategy  76.1887  9431  
13  jarabas  CMADE  Covariance Matrix Adaptation Differential Evolution  33.1138  10561 
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.