investigating the impact of sequential selection in the (1,2)-cma-es on the noiseless bbob-2010 testbed
This paper investigates the impact of sequential selection, a concept recently introduced for Evolution Strategies (ESs), that consists in performing the evaluations of the different candidate solutions sequentially, concluding the iteration immediately if one offspring is better than the parent. The performance of the (1,2)-Covariance-Matrix-Adaptation Evolution-Strategy (CMA-ES) is compared to the performance of the (1,2 s )-CMA-ES where sequential selection is implemented on the BBOB-2010 noiseless benchmark testbed. For each strategy, an independent restart mechanism is implemented. A total budget of 10 4 D function evaluations per trial has been used, where D is the dimension of the search space. The experiments do not allow a general statement regarding a statistically significant difference between the two algorithms and we conclude that the sequential selection has no impact on the performance of the (1,2)-CMA-ES on the noiseless BBOB-2009 testbed.
benchmarking black-box optimization