@inproceedings{46396,
abstract = {{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.}},
author = {{Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}},
booktitle = {{Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}},
isbn = {{9781450311779}},
keywords = {{machine learning, exploratory landscape analysis, fitness landscape, benchmarking, evolutionary optimization, bbob test set, algorithm selection}},
pages = {{313–320}},
publisher = {{Association for Computing Machinery}},
title = {{{Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning}}},
doi = {{10.1145/2330163.2330209}},
year = {{2012}},
}