A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer Reviewe

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem

Semi-automatic spline fitting of planar curvilinear profiles in digital images using the Hough transform

We develop a novel method for the recognition of curvilinear profiles in digital images. The proposed method, semi-automatic for both closed and open planar profiles, essentially consists of a preprocessing step exploiting an edge detection algorithm, and a main step involving the Hough transform technique. In the preprocessing step, a Canny edge detection algorithm is applied in order to obtain a reduced point set describing the profile curve to be reconstructed. Also, to identify in the profile possible sharp points like cusps, we additionally use an algorithm to find the approximated tangent vector of every edge point. In the subsequent main step, we then use a piecewisely defined Hough transform to locally recognize from the point set a low-degree piecewise polynomial curve. The final outcome of the algorithm is thus a spline curve approximating the underlined profile image. The output curve consists of polynomial pieces connected G^1 continuously, except in correspondence of the identified cusps, where the order of continu- ity is only C^0 , as expected. To illustrate effectiveness and efficiency of the new profile detection technique we present several numerical results dealing with detection of open and closed profiles in images of dif- ferent type, i.e., medical and photographic image