在BBOB-2012噪声测试平台上对IPOP-saACM-ES进行黑盒优化基准测试
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed
摘要：
本文研究了IPOP-saACM-ES算法的性能，提出了一种自适应代理辅助协方差矩阵自适应演化策略。使用重启测试算法，直到达到$10^6D$的函数求值的总数，其中$D$是函数搜索空间的维度。实验结果表明，在6个具有中等噪声的基准问题上，代理模型控制使IPOP-saACM-ES具有与原IPOP-aCMA-ES一样的鲁棒性，且性能优于后者2 ~ 3倍.在维度20的30个基准问题中，IPOP-saACM-ES超过了BBOB-2009和BBOB-2010期间观察到的记录。
英文摘要：
In this paper, we study the performance of IPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy. The algorithm was tested using restarts till a total number of function evaluations of $10^6D$ was reached, where $D$ is the dimension of the function search space. The experiments show that the surrogate model control allows IPOP-saACM-ES to be as robust as the original IPOP-aCMA-ES and outperforms the latter by a factor from 2 to 3 on 6 benchmark problems with moderate noise. On 15 out of 30 benchmark problems in dimension 20, IPOP-saACM-ES exceeds the records observed during BBOB-2009 and BBOB-2010.
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