Unified Framework for the Adaptive Operator Selection of Discrete Parameters
This work provides a structured approach for researchers in evolutionary computation to better understand and apply adaptive operator selection, though it is incremental as it builds upon existing categorizations.
The authors tackled the problem of adaptive operator selection in evolutionary algorithms by creating a unified framework that simplifies and generalizes existing methods, and they tested it on the BBOB test bed with hyperparameter tuning using IRACE, showing performance comparisons across three sets of experiments.
We conduct an exhaustive survey of adaptive selection of operators (AOS) in Evolutionary Algorithms (EAs). We simplified the AOS structure by adding more components to the framework to built upon the existing categorisation of AOS methods. In addition to simplifying, we looked at the commonality among AOS methods from literature to generalise them. Each component is presented with a number of alternative choices, each represented with a formula. We make three sets of comparisons. First, the methods from literature are tested on the BBOB test bed with their default hyper parameters. Second, the hyper parameters of these methods are tuned using an offline configurator known as IRACE. Third, for a given set of problems, we use IRACE to select the best combination of components and tune their hyper parameters.