AClib: A Benchmark Library for Algorithm Configuration

Frank Hutter, Manuel López-Ibáñez, Chris Fawcett, Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle

Abstract: Modern solvers for hard computational problems often expose parameters that permit customization for high performance on specific instance types. Since it is tedious and time-consuming to manually optimize such highly parameterized algorithms, recent work in the AI literature has developed automated approaches for this algorithm configuration problem [1, 3, 10, 11, 13, 16].