论文标题
具有自调节不对称突变的进化算法
Evolutionary Algorithms with Self-adjusting Asymmetric Mutation
论文作者
论文摘要
进化算法（EAS）和其他随机搜索启发式方法通常被认为是无偏见的算法，相对于基础搜索空间的不同变换而言是不变的。但是，如果可以使用一定数量的域知识，则可以在EAS中使用有偏见的搜索操作员。我们考虑一个简单的（1+1）EA用于二进制搜索空间，并分析一个不对称的突变算子，该突变算子可以对零和一位的处理方式不同。该操作员通过允许操作员根据算法的成功率变化，扩展了Jansen和Sudholt（ECJ 18（1），2010年）的先前工作。使用一个自我调整方案，学习适当程度的不对称程度，我们在功能类别上显示了改进的运行时结果，onemax $ _a $描述了固定目标$ a \ in \ in \ {0,1 \}^n $的匹配位数。
Evolutionary Algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if a certain amount of domain knowledge is available the use of biased search operators in EAs becomes viable. We consider a simple (1+1) EA for binary search spaces and analyze an asymmetric mutation operator that can treat zero- and one-bits differently. This operator extends previous work by Jansen and Sudholt (ECJ 18(1), 2010) by allowing the operator asymmetry to vary according to the success rate of the algorithm. Using a self-adjusting scheme that learns an appropriate degree of asymmetry, we show improved runtime results on the class of functions OneMax$_a$ describing the number of matching bits with a fixed target $a\in\{0,1\}^n$.
