Benchmarking a Weighted Negative Covariance Matrix Update on the BBOB-2010 Noisy Testbed

Nikolaus Hansen (1) , Raymond Ros (1)

1 TAO - Machine Learning and Optimisation

In a companion paper, we presented a weighted negative update of the covariance matrix in the CMA-ES—weighted active CMA-ES or, in short, aCMA-ES. In this paper, we benchmark the IPOP-aCMA-ES on the BBOB-2010 noisy testbed in search space dimension between 2 and 40 and compare its performance with the IPOP-CMA-ES. The aCMA suffers from a moderate performance loss, of less than a factor of two, on the sphere function with two different noise models. On the other hand, the aCMA enjoys a (significant) performance gain, up to a factor of four, on 13 unimodal functions in various dimensions, in particular the larger ones. Compared to the best performance observed during BBOB-2009, the IPOP-aCMA-ES sets a new record on overall ten functions. The global picture is in favor of aCMA which might establish a new standard also for noisy problems.

Domaines: Optimisation et contrôle [math.OC]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]

Nikolaus Hansen, Raymond Ros. Benchmarking a Weighted Negative Covariance Matrix Update on the BBOB-2010 Noisy Testbed. Genetic And Evolutionary Computation Conference, Jul 2010, Portland, United States. ⟨hal-00545729⟩