论文标题
降低差异以在连续域中更好地采样
Variance Reduction for Better Sampling in Continuous Domains
论文作者
论文摘要
实验的设计，随机搜索，基于群体的方法的初始化或在进化算法时期内的采样时使用根据某些概率分布绘制的样品来近似最佳位置。最近的论文表明，用于采样的最佳搜索分布可能比先前的分布对最佳位置的不确定性进行建模可能更峰值。我们确认该声明，根据人口尺寸$λ$和尺寸$ d $为搜索分配提供明确的值，并通过实验验证我们的结果。
Design of experiments, random search, initialization of population-based methods, or sampling inside an epoch of an evolutionary algorithm use a sample drawn according to some probability distribution for approximating the location of an optimum. Recent papers have shown that the optimal search distribution, used for the sampling, might be more peaked around the center of the distribution than the prior distribution modelling our uncertainty about the location of the optimum. We confirm this statement, provide explicit values for this reshaping of the search distribution depending on the population size $λ$ and the dimension $d$, and validate our results experimentally.
