investigating the impact of sequential selection in the (1,2)-cma-es on the noisy bbob-2010 testbed
Sequential selection was introduced for Evolution Strategies (ESs) with the aim of accelerating their convergence--performing the evaluations of the different offspring sequentially and concluding an iteration immediately if one offspring is better than the parent. This paper investigates the impact of the application of sequential selection to the (1,2)-CMA-ES on the BBOB-2010 noisy benchmark testbed. The performance of the (1,2 s )-CMA-ES, where sequential selection is implemented, is compared to the baseline algorithm (1,2)-CMA-ES. Independent restarts for the two algorithms are conducted up to a maximum number of 10 4 D function evaluations, where D is the dimension of the search space. The results show a slight improvement of the (1,2 s )-CMA-ES over the baseline (1,2)-CMA-ES on the sphere function with Cauchy noise and a stronger decline on the sphere function with moderate uniform noise. Overall, the (1,2 s )-CMA-ES seems slighly less reliable and we conclude that for the (1,2)-CMA-ES, sequential selection is no improvement on noisy functions.
benchmarking black-box optimization