Learning Techniques for Automatic Algorithm Portfolio Selection

The purpose of this paper is to show that a well known machine learning technique based on Decision Trees can be effectively used to select the best approach (in terms of efficiency) in an algorithm portfolio for a particular case study: the Bid Evaluation Problem (BEP) in Combinatorial Auctions. In particular, we are interested in deciding when to use a Constraint Programming (CP) approach and when an Integer Programming (IP) approach, on the basis of the structure of the instance considered. Different instances of the same problem present a different structure, and one aspect (e.g. feasibility or optimality) can prevail on the other. We have extracted from a set of BEP instances, a number of parameters representing the instance structure. Some of them (few indeed) precisely identify the best strategy and its corresponding tuning to be used to face that instance. We will show that this approach is very promising, since it identifies the most efficient algorithm in the 90% of the cases.