论文标题
$（1+（λ，λ））$全局半算法
The $(1+(λ,λ))$ Global SEMO Algorithm
论文作者
论文摘要
$（1+（λ，λ））$遗传算法是最近提出的具有几种有趣属性的单目标进化算法。我们表明，它的主要工作原理，高率的突变，也可以将其作为修复机制作为修复机制，也可以运输到多目标进化计算中。我们定义了$（1+（λ，λ））$全局半算法，这是经典全局半算法的变体，并证明它比全局半月更快地优化了OneMinmax基准测试速度。按照单一目标示例，我们设计了一个五分之一的启发性动态参数设置（在离散的多目标优化方面，我们首次获得了最佳知识），并证明它将运行时进一步提高到$ O（n^2）$，而全球SEMO的最佳运行时保证只有$ O（N^2 \ log n^2 \ log n）$。
The $(1+(λ,λ))$ genetic algorithm is a recently proposed single-objective evolutionary algorithm with several interesting properties. We show that its main working principle, mutation with a high rate and crossover as repair mechanism, can be transported also to multi-objective evolutionary computation. We define the $(1+(λ,λ))$ global SEMO algorithm, a variant of the classic global SEMO algorithm, and prove that it optimizes the OneMinMax benchmark asymptotically faster than the global SEMO. Following the single-objective example, we design a one-fifth rule inspired dynamic parameter setting (to the best of our knowledge for the first time in discrete multi-objective optimization) and prove that it further improves the runtime to $O(n^2)$, whereas the best runtime guarantee for the global SEMO is only $O(n^2 \log n)$.
