Algorithm Configuration in the Cloud: A Feasibility Study

Daniel Geschwender, Frank Hutter, Lars Kotthoff, Yuri Malitsky, Holger H. Hoos, Kevin Leyton-Brown

Abstract: Configuring algorithms automatically to achieve high performance is becoming increasingly relevant and important in many areas of academia and industry. Algorithm configuration methods take a parameterized target algorithm, a performance metric and a set of example data, and aim to find a parameter configuration that performs as well as possible on a given data set.