Evolutionary algorithms are known to be robust to noise in the evaluation of the fitness. In particular, larger offspring population sizes often lead to strong robustness. We analyze to what extent the $(1+(\lambda,\lambda))$ genetic algorithm is robust to noise. This algorithm also works with larger offspring population sizes, but an intermediate selection step and a non-standard use of crossover as repair mechanism could render this algorithm less robust than, e.g., the simple $(1+\lambda)$ evolutionary algorithm. Our experimental analysis on several classic benchmark problems shows that this difficulty does not arise. Surprisingly, in many situations this algorithm is even more robust to noise than the $(1+\lambda)$~EA.

翻译：进化算法已知对适应度评估中的噪声具有鲁棒性。特别是，较大的后代种群规模往往能带来更强的鲁棒性。我们分析了$(1+(\lambda,\lambda))$遗传算法对噪声的鲁棒程度。该算法也使用较大的后代种群规模，但中间选择步骤以及将交叉作为修复机制的非标准应用可能使其鲁棒性低于（例如）简单的$(1+\lambda)$进化算法。我们对多个经典基准问题的实验分析表明，这一困难并未出现。令人惊讶的是，在许多情况下，该算法甚至比$(1+\lambda)$~进化算法对噪声更具鲁棒性。