AAS 390 Chapter Notes - Chapter 1: Rela, Ion, Runtime System

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Lin xu, frank hutter, jonathan shen, holger h. hoos and kevin leyton-brown. Empirical studies often observe that the performance of different algorithms across problem instance distribu- tions can be quite uncorrelated. When this occurs, there is an incentive to investigate the use of portfolio-based approaches that draw on the strengths of multiple algo- rithms. Different from previous versions of satzilla, which utilized empirical hardness models [4, 6] for estimating each candidate algorithm"s performance on a given sat instance, satzilla2012 is based on cost-sensitive clas- si cation models [7]. We also introduced a new proce- dure that generates a stand-alone satzilla executable based on models learned within matlab. Finally, we used new component algorithms and training instances. Overall, satzilla2012 makes use of the same methodology as described in [9]. We use the term performance score to refer to the quantity we aim to optimize: automatically determine the best-scoring combina- tion of pre-solvers and their corresponding perfor- mance score.

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