Low-Knowledge Algorithm Control

This paper addresses the question of allocating computational resources among a set of algorithms in order to achieve the best performance on a scheduling problem instance. Our primary motivation in addressing this problem is to reduce the expertise needed to apply constraint technology. Therefore, we investigate algorithm control techniques that make decision based only on observations of the improvement in solution quality achieved by each algorithm. We call our approach “low-knowledge” since it does not rely on complex prediction models. We show that such an approach results in a system that achieves significantly better performance than all of the pure algorithms without requiring additional human expertise. Furthermore the low knowledge approach achieves performance equivalent to a perfect high-knowledge classifi-