Results of the GECCO'2017 1OBJ 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 17 participants in the field. The best function value per problem and participant (1000 times 17 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  nbelkhir  ASACCMAES  Per Instance Algorithm Selection for low dimension and Per Instance Algorithm Configuration of CMAES based on problem features extracted a uniform sample of the optimization problem.  link  1068.28  5257  
2  LB  aDTSCMAES  Doubly trained surrogate CMAES (DTSCMAES) with basic selfadaptation of the number of originalevaluated points per generation. Switch to standard IPOPCMAES after roughly 4 days of computation, or sooner if the run ecountered BBComp server breakdown. CMAES tries to restart from unexplored regions.  857.317  5506  
3  Simon Wessing  Twostage algorithms  link  712.951  5582  
4  bujok  IDEbdQ 

682.493  5800  
5  radka  641.706  5940  
6  Poly Montreal  562.877  7721  
7  Artelys  Artelys Knitro  Artelys Knitro used in derivativefree mode with multistart  link  link  521.683  8017  
8  anonymous  487.364  7273  
9  djagodzi  DES  Differential Evolution Strategy  339.278  8083  
10  Ralf S.  Mix of PSO and GA  279.05  9382  
11  Al Jimenez  270.072  9382  
12  anonymous953  266.485  9494  
13  jarabas  CMADE  Covariance Matrix Adaptation Differential Evolution  203.632  9930  
14  EMAGIN's Tomcat  Emagin's Tomcat (Sparrow mode)  Developed by Mohammadamin Jahanpour  74.9573  11887  
15  SebastianGer  Initial LHCbased exploitation followed by several short (1+1)EA runs and pure (1+1)EA on best result  27.9964  14791  
16  Jeremy M  Custom algorithm  19.8681  14764  
17  bAIz  Adaptive Wind Driven Optimization  Adaptive Wind Driven Optimization (AWDO) is upgraded version of the Wind Driven Optimization (WDO), where the inherent parameters of the WDO are adaptively selected by CMAES method at each iteration. The WDO algorithm is population based natureinspired algorithm inspired by the atmospheric dynamics equations driving the motion of the wind. The utilization of the actual physical equations makes the algorithm both realistic in nature and very efficient. Check out the homepage at http://www.thewdo.com  link  link  14.1716  14050 
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.