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Meta-heuristics are of significant interest to decision-makers due to the capability of finding good solutions for complex problems within a reasonable amount of computational time. These methods are further known to perform according to how their algorithm-specific parameters are set. As most practitioners aim for an off-the-shelf approach when using meta-heuristics, they require an easy appli...

Object tracking quality usually depends on video scene conditions (e.g. illumination, density of objects, object occlusion level). In order to overcome this limitation, this article presents a new control approach to adapt the object tracking process to the scene condition variations. More precisely, this approach learns how to tune the tracker parameters to cope with the tracking context varia...

I n this chapter we discuss the notion of Evolutionary Algorithm (EA) parameters and propose a distinction between EAs and EA instances, based on the type of parameters used to specify their details. Furthermore, we consider the most important aspects of the parameter tuning problem and give an overview of existing parameter tuning methods. Finally, we elaborate on the methodological issues inv...

The central air condition system is a complex system. Aimed at the puzzle of optimal status adjusting by once setting parameter of fuzzy PID, the paper proposed a sort of parameter auto-tuning method of fuzzy-PID based on self-learning algorithm. It adopted parameter autotuning technique to adjust the PID parameters in real time so as to ensure good quality of control system. It combined fuzzy ...

In this paper, a framework for the simplification and standardization of metaheuristic related parameter-tuning by applying a four phase methodology, utilizing Design of Experiments and Artificial Neural Networks, is presented. Metaheuristics are multipurpose problem solvers that are utilized on computational optimization problems for which no efficient problem specific algorithm exist. Their s...

Meta-heuristic algorithms should be compared using the best parameter values for all the involved algorithms. However, this is often unrealised despite the existence of several parameter tuning approaches. In order to further popularise tuning, this paper introduces a new tuning method CRS-Tuning that is based on meta-evolution and our novel method for comparing and ranking evolutionary algorit...

Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can largely influence behavior algorithm under consideration. Thus, proper tuning should be carried out to ensure used for may perform well sufficiently robust solving different types problems. This chapter reviews some main methods then highlights important issues concerning latest ...

We develop an approach to tuning of penalized regression variable selection methods by calculating the sparsest estimator contained in a confidence region of a specified level. Because confidence intervals/regions are generally understood, tuning penalized regression methods in this way is intuitive and more easily understood by scientists and practitioners. More importantly, our work shows tha...