Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Mixed models for the analysis of local search components
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
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Decision trees are widely used to represent information extracted from data sets. In studies on heuristics for optimization, there are two types of information in which we may be interested: how the parameters of the algorithm affect its performance and which characteristics of the instances determine a difference in the performance of the algorithms. Tree-based learning algorithms, as they exist in several software packages, do not allow to model thoroughly experimental designs for answering these types of questions. We try to overcome this issue and devise a new learning algorithm for the specific settings of analysis of optimization heuristics.