论文标题
在动态环境中，大量的人口大小和交叉有助于
Large Population Sizes and Crossover Help in Dynamic Environments
论文作者
论文摘要
HyperCube上的动态线性函数是为每个位分配的函数，但权重随时间而变化。在整个优化过程中，这些功能保持相同的全局最佳效果，并且从未存在局部优势。尽管如此，最近显示[Lengler，Schaller，Foci 2019]，$（1+1）$ - 进化算法需要指数时间才能找到或近似某些算法配置的最佳时间。在本文中，我们研究了较大的人口大小对动态纤维的影响，动态纤维是动态线性函数的极端形式。我们发现，中等增加的人口量扩展了有效算法配置的范围，并且分频器大大提高了这种积极效果。值得注意的是，类似于[Lengler，Zou，Foga 2019]中单调函数的静态设置，对于$（μ+1）$ - EA- EA的优化最难的区域并不接近最佳，但远离它。相反，对于$（μ+1）$ -GA，最佳区域是所有研究的情况下最困难的区域。
Dynamic linear functions on the hypercube are functions which assign to each bit a positive weight, but the weights change over time. Throughout optimization, these functions maintain the same global optimum, and never have defecting local optima. Nevertheless, it was recently shown [Lengler, Schaller, FOCI 2019] that the $(1+1)$-Evolutionary Algorithm needs exponential time to find or approximate the optimum for some algorithm configurations. In this paper, we study the effect of larger population sizes for Dynamic BinVal, the extremal form of dynamic linear functions. We find that moderately increased population sizes extend the range of efficient algorithm configurations, and that crossover boosts this positive effect substantially. Remarkably, similar to the static setting of monotone functions in [Lengler, Zou, FOGA 2019], the hardest region of optimization for $(μ+1)$-EA is not close the optimum, but far away from it. In contrast, for the $(μ+1)$-GA, the region around the optimum is the hardest region in all studied cases.
