The algorithm selection problem [Rice 1976] seeks to answer the question: Which algorithm
is likely to perform best for my problem? Recognizing the problem as a learning task in the …

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a
previously unseen input, using machine learning techniques to build a model of the …

It has been widely observed that there is no single" dominant" SAT solver; instead, different
solvers perform best on different instances. Rather than following the traditional approach of …

The AI community has achieved great success in designing high-performance algorithms for
hard combinatorial problems, given both considerable domain knowledge and considerable …

It is well known that the ratio of the number of clauses to the number of variables in a random
k-SAT instance is highly correlated with the instance's empirical hardness. We consider the …

Is it possible to predict how long an algorithm will take to solve a previously-unseen instance
of an NP-complete problem? If so, what uses can be found for models that make such …

It has been widely observed that there is no “dominant” SAT solver; instead, different solvers
perform best on different instances. Rather than following the traditional approach of …

Complete tree search is a highly effective method for tackling Mixed-Integer Programming
(MIP) problems, and over the years, a plethora of branching heuristics have been introduced …

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) …

Even though it is well known that for most relevant computational problems, different
algorithms may perform better on different classes of problem instances, most researchers …