The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a state of the art evolutionary algorithm that leverages linkage learning to efficiently exploit problem structure. By identifying and preserving important building blocks during variation, GOMEA has shown promising performance on various optimization problems. In this paper, we provide the first runtime analysis of GOMEA on the concatenated trap function, a challenging benchmark problem that consists of multiple deceptive subfunctions. We derived an upper bound on the expected runtime of GOMEA with a truthful linkage model, showing that it can solve the problem in $O(m^{3}2^k)$ with high probability, where $m$ is the number of subfunctions and $k$ is the subfunction length. This is a significant speedup compared to the (1+1) EA, which requires $O(ln{(m)}(mk)^{k})$ expected evaluations.

翻译：基因池最优混合进化算法（GOMEA）是一种先进的进化算法，它利用连锁学习来高效地利用问题结构。通过在变异过程中识别并保留重要的构建块，GOMEA在各种优化问题上展现出了良好的性能。本文首次对GOMEA在级联陷阱函数上的运行时间进行了分析，该函数是一个由多个欺骗性子函数构成的具有挑战性的基准问题。我们推导了GOMEA在真实连锁模型下的期望运行时间上界，证明其能以高概率在$O(m^{3}2^k)$时间内求解该问题，其中$m$为子函数数量，$k$为子函数长度。与需要$O(ln{(m)}(mk)^{k})$次期望评估的(1+1) EA相比，这是一个显著的加速。