Landscape-Aware Constraint Handling Applied to Differential Evolution

In real-world contexts optimisation problems frequently have constraints. Evolutionary algorithms do not naturally handle constrained spaces, so require constraint handling techniques to modify the search process. Based on the thesis that different constraint handling approaches are suited to different problem types, this study shows that the features of the problem can provide guidance in choosing appropriate constraint handling techniques for differential evolution. High level algorithm selection rules are derived through data mining based on a training set of problems on which landscape analysis is performed through sampling. On a set of different test problems, these rules are used to switch between constraint handling techniques during differential evolution search using on-line analysis of landscape features. The proposed landscape-aware switching approach is shown to out-perform the constituent constraint-handling approaches, illustrating that there is value in monitoring the landscape during search and switching to appropriate techniques depending on the problem characteristics. Results are also provided that show that the approach is fairly insensitive to parameter changes.