Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired
by natural evolution. In recent years the field of evolutionary computation has
developed a rigorous analytical theory to analyse their runtime on many
illustrative problems. Here we apply this theory to a simple model of natural
evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the
time between occurrence of new mutations is much longer than the time it takes
for a new beneficial mutation to take over the population. In this situation,
the population only contains copies of one genotype and evolution can be
modelled as a (1+1)-type process where the probability of accepting a new
genotype (improvements or worsenings) depends on the change in fitness.
We present an initial runtime analysis of SSWM, quantifying its performance
for various parameters and investigating differences to the (1+1)EA. We show
that SSWM can have a moderate advantage over the (1+1)EA at crossing fitness
valleys and study an example where SSWM outperforms the (1+1)EA by taking
advantage of information on the fitness gradient.

本文分析了强选择弱突变（SSWM）进化模型运行时间及其与（1 + 1）EA 的异同之处，并研究了如何利用适应度梯度实现 SSWM 在跨越适应度谷时的优势。