TY - CONF
AB - 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.
AU - Bischl, Bernd
AU - Mersmann, Olaf
AU - Trautmann, Heike
AU - Preuß, Mike
ID - 46396
KW - machine learning
KW - exploratory landscape analysis
KW - fitness landscape
KW - benchmarking
KW - evolutionary optimization
KW - bbob test set
KW - algorithm selection
SN - 9781450311779
T2 - Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation
TI - Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning
ER -