论文标题
Onemax并不是适应性改进的最简单功能
OneMax is not the Easiest Function for Fitness Improvements
论文作者
论文摘要
我们研究$（1：s+1）$成功规则，用于控制$（1，λ）$ - ea的人口规模。 Hevia Fajardo和Sudholt显示，如果健身景观太容易，则该参数控制机制可能会遇到问题。他们推测，对于OneMax基准，这个问题是最糟糕的，因为在某些良好的意义上，Onemax是最简单的健身景观。在本文中，我们反驳了这一猜想，并表明Onemax不是找到改进步骤的最简单的健身景观。 结果，我们表明存在$ s $和$ \ varepsilon $，使得自调整的$（1，λ）$ -Ea，$（1：s+1）$ - 规则在以$ \ varepsilon n $ Zero-bits启动时有效地优化了Onemax，但在Dynamial bydynomial by dynymial bimimial bimime tyny dynymial bimimial night of dynamimial bimial n ofdymial bimial bimax n ofdymial bimax bemax $ a。因此，我们表明在某些景观中，$（1：s+1）$的问题 - 控制$（1，λ）$的人口大小的规则比OneMax更为严重。
We study the $(1:s+1)$ success rule for controlling the population size of the $(1,λ)$-EA. It was shown by Hevia Fajardo and Sudholt that this parameter control mechanism can run into problems for large $s$ if the fitness landscape is too easy. They conjectured that this problem is worst for the OneMax benchmark, since in some well-established sense OneMax is known to be the easiest fitness landscape. In this paper we disprove this conjecture and show that OneMax is not the easiest fitness landscape with respect to finding improving steps. As a consequence, we show that there exists $s$ and $\varepsilon$ such that the self-adjusting $(1,λ)$-EA with $(1:s+1)$-rule optimizes OneMax efficiently when started with $\varepsilon n$ zero-bits, but does not find the optimum in polynomial time on Dynamic BinVal. Hence, we show that there are landscapes where the problem of the $(1:s+1)$-rule for controlling the population size of the $(1, λ)$-EA is more severe than for OneMax.
