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.

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	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
4	bujok	IDEbdQ	"IDE" - DE with individual dependent parameter settings "b" - Enhanced mutation with the best base point in the (second) exploitation stage "d" - diversity control based on the standard deviation of the points in the population (population size is changed inside of Nmin and Nmax boundaries) "Q" - Nmax boundary from population size update is decreased by half in each quarter of search process			682.493	5800
5	radka					641.706	5940
6	Poly Montreal					562.877	7721
7	Artelys	Artelys Knitro	Artelys Knitro used in derivative-free 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 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.