Evolutionary Algorithms with Self-adjusting Asymmetric Mutation
This work is incremental, enhancing evolutionary algorithms for binary optimization by adapting mutation asymmetry to improve efficiency.
The paper tackled the problem of improving evolutionary algorithms by introducing a self-adjusting asymmetric mutation operator that adapts based on success rates, resulting in improved runtime results on the OneMax_a function class.
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 $a\in\{0,1\}^n$.