Results of the GECCO'2017 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 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.
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||nbelkhir||AS-AC-CMA-ES||Per Instance Algorithm Selection for low dimension and Per Instance Algorithm Configuration of CMA-ES based on problem features extracted a uniform sample of the optimization problem.||link||1068.28||5257|
|2||LB||aDTS-CMA-ES||Doubly trained surrogate CMA-ES (DTS-CMA-ES) with basic self-adaptation of the number of original-evaluated points per generation. Switch to standard IPOP-CMA-ES after roughly 4 days of computation, or sooner if the run ecountered BBComp server breakdown. CMA-ES tries to restart from unexplored regions.||857.317||5506|
|3||Simon Wessing||Two-stage algorithms||link||712.951||5582|
|7||Artelys||Artelys Knitro||Artelys Knitro used in derivative-free mode with multistart||link||link||521.683||8017|
|9||djagodzi||DES - Differential Evolution Strategy||339.278||8083|
|10||Ralf S.||Mix of PSO and GA||279.05||9382|
|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 LHC-based 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 CMA-ES method at each iteration. The WDO algorithm is population based nature-inspired 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.