Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbed

We implement a weighted negative update of the covariance matrix in the CMA-ES--weighted active CMA-ES or, in short, aCMA-ES. We benchmark the IPOP-aCMA-ES and compare the performance with the IPOP-CMA-ES on the BBOB-2010 noiseless testbed in dimensions between 2 and 40. On nine out of 12 essentially unimodal functions, the aCMA is faster than CMA, in particular in larger dimension. On at least three functions it also leads to a (slightly) better scaling with the dimension. In none of the 24 benchmark functions aCMA appears to be significantly worse in any dimension. On two and five functions, IPOP-CMA-ES and IPOP-aCMA-ES respectively exceed the record observed during BBOB-2009.