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

A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic
methods applicable to a wide set of optimization problems for which exact/analytical …

Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

Algorithm selection (AS) techniques-which involve choosing from a set of algorithms the one
expected to solve a given problem instance most efficiently-have substantially improved the …

The particle swarm optimization (PSO) algorithm has been the object of many studies and
modifications for more than 25 years. Ranging from small refinements to the incorporation of …

The design and development of metaheuristic algorithms can be time-consuming and
difficult for a number of reasons including the complexity of the problems being tackled, the …

Metaheuristic algorithms have become one of the preferred approaches for solving
optimization problems. Finding the best metaheuristic for a given problem is often difficult …

Stochastic local search methods are at the core of many effective heuristics for tackling
different permutation flowshop problems (PFSPs). Usually, such algorithms require a careful …

Industrial production scheduling problems are challenges that researchers have been trying
to solve for decades. Many practical scheduling problems such as the hybrid flowshop are …

Search techniques have been successfully applied for solving different software testing
problems. However, choosing, implementing and configuring a search technique can be …