Parameter Tuning for Local-Search-Based Matheuristic Methods

Descent Methods for Tuning Parameter Refinement

This paper addresses multidimensional tuning parameter selection in the context of “train-validate-test” and K-fold cross validation. A coarse grid search over tuning parameter space is used to initialize a descent method which then jointly optimizes over variables and tuning parameters. We study four regularized regression methods and develop the update equations for the corresponding descent ...

A variable neighborhood search based matheuristic for nurse rostering problems

A practical nurse rostering problem, which arises at a ward of an Italian private hospital, is considered. In this problem, it is required each month to generate the nursing staff shifts subject to various requirements. A matheuristic approach is introduced, based on a set of neighborhoods searched by a commercial integer programming solver within a defined global time limit. Generally speaking...

Instance-Based Parameter Tuning via Search Trajectory Similarity Clustering

This paper is concerned with automated tuning of parameters in local-search based meta-heuristics. Several generic approaches have been introduced in the literature that returns a ”one-size-fits-all” parameter configuration for all instances. This is unsatisfactory since different instances may require the algorithm to use very different parameter configurations in order to find good solutions....

Tuning Local Search for Satisfiability Testing

Efficient and Robust Parameter Tuning for Heuristic Algorithms

The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristi...