Constraint programming is a powerful paradigm for solving combinatorial search problems
that draws on a wide range of techniques from artificial intelligence, computer science …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

The identification of performance-optimizing parameter settings is an important part of the
development and application of algorithms. We describe an automatic framework for this …

The previous chapter has discussed how hierarchy can be used to reduce problem
complexity in black-box optimization. Additionally, the chapter has identified the three …

SATLIB is an online resource for SAT-related research established in June 1998. Its core
components, a benchmark suite of SAT instances and a collection of SAT solvers, aim to …

We present a new method for instance-specific algorithm configuration (ISAC). It is based on
the integration of the algorithm configuration system GGA and the recently proposed …

The determination of appropriate values for free algorithm parameters is a challenging and
tedious task in the design of effective algorithms for hard problems. Such parameters include …

Designing high-performance solvers for computationally hard problems is a difficult and
often time-consuming task. Although such design problems are traditionally solved by the …

Abstract Machine learning can be used to build models that predict the run-time of search
algorithms for hard combinatorial problems. Such empirical hardness models have …

There are four primary goals of this dissertation. First, design a competent optimization
algorithm capable of learning and exploiting appropriate problem decomposition by …