This paper surveys the recent attempts, both from the machine learning and operations
research communities, at leveraging machine learning to solve combinatorial optimization …

Since automatic algorithm configuration methods have been very effective, recently there is
increasing research interest in utilizing them for automatic solver construction, resulting in …

Modern SAT solvers have experienced a remarkable progress on solving industrial
instances. It is believed that most of these successful techniques exploit the underlying …

The present paper offers an analysis strategy to examine benchmark sets of combinatorial
search problems. Experimental analysis has been widely used to compare a set of …

This study investigates how to schedule nanosatellite tasks more efficiently using Graph
Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the …

Optimisation algorithms require careful tuning and analysis to perform well in practice. Their
performance is strongly affected by algorithm parameter choices, software, and hardware …

Machine learning (ML) has been actively adopted in Linear Programming (LP) and Mixed-
Integer Linear Programming (MILP), whose potential is hindered by instance scarcity …

Genetic improvement (GI) uses automated search to find improved versions of existing
software. If over the years the potential of many GI approaches have been demonstrated, the …

Abstract Software Product Lines (SPLs) are a mechanism for large-scale reuse where
families of related software systems are represented in terms of commonalities and …