Algorithm selection based on exploratory landscape analysis and cost-sensitive learning
Bischl B., Mersmann O., Trautmann H., Preuss M.
The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, specially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB'09/10 workshop. © 2012 ACM.
algorithm selection; bbob test set; benchmarking; evolutionary optimization; exploratory landscape analysis; fitness landscape; machine learning