In this paper, we study two approaches for tuning parameters of optimization algorithms: off-line and on-line tuning. The goal of tuning is to find an appropriate configuration of an algorithm, where a configuration is a specific setting of all parameters. In off-line tuning, the appropriate configuration is chosen after a preliminary phase. In on-line tuning, the configuration might be changed...

in recent years, soft computing methods have generated a large research interest. the synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. a particular evolutionary algorithm (ea) is differential evolution (de). as for any ea, de algorithm also requires parameters tuning to achieve desirable performance. in this paper tuning the perturbation factor vector of de ...

We consider the problem of selecting and tuning learning parameters of support vector machines, especially for the classification of large and unbalanced data sets. We show why and how simple models with few parameters should be refined and propose an automated approach for tuning the increased number of parameters in the extended model. Based on a sensitive quality measure we analyze correlati...

A virtualized multi-core environment is a multi-core machine running virtual machines with multiple virtual cpus. Previous research has demonstrated that the performance of multi-threaded, disk intensive, and network intensive applications in such an environment can be greatly improved by correctly tuning certain simple multiprocessing related system parameters like cpu affinity and interrupt a...

One of the most basic functions of control engineers is tuning of controllers. There are always several process loops in the plant necessitate of tuning. The auto tuned Proportional Integral Derivative (PID) Controllers are designed for applications where large load changes are expected or the need for extreme accuracy and fast response time exists. The algorithm presented in this paper is used...

This thesis presents a set of rigorous methodologies for tuning the performance of algorithms that solve optimisation problems. Many optimisation problems are difficult and time-consuming to solve exactly. An alternative is to use an approximate algorithm that solves the problem to an acceptable level of quality and provides such a solution in a reasonable time. Using optimisation algorithms ty...

Tuning controller parameters is a recurring and time-consuming problem in control. This is especially true in the field of adaptive control, where good performance is typically only achieved after significant tuning. Recently, it has been shown that constrained Bayesian optimization is a promising approach to automate the tuning process without risking system failures during the optimization pr...

We present a dynamic method for tuning algorithmic parameters of parallel scientific programs. By treating tuning as a separate concern in the software development process, our method supports personalized development of optimization schemes for existing programs that are not easily supported by conventional tuning techniques. We use a compositional framework to transparently combine tuning cod...

PID control schemes based on classical control theory, have been widely applied to real control systems. There have been lots of works on tuning PID parameters, since the control performance strongly depends on these parameters. Several self-tuning and auto-tuning PID techniques have been reported for systems with unknown or slowly time-varing parameters. None, however, is applicable to discret...

In recent years, soft computing methods have generated a large research interest. The synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. A particular evolutionary algorithm (EA) is differential evolution (DE). As for any EA, DE algorithm also requires parameters tuning to achieve desirable performance. In this paper tuning the perturbation factor vector of DE ...