For a learning automaton, a proper configuration of its learning parameters, which are crucial for the automaton’s performance, is relatively difficult due to the necessity of a manual parameter tuning before real applications. To ensure a stable and reliable performance in stochastic environments, parameter tuning can be a time-consuming and interactioncosting procedure in the field of LA. Esp...

Tuning methods for selecting appropriate parameter configurations of optimization algorithms have been the object of several recent studies. The selection of the appropriate configuration may strongly impact on the performance of evolutionary algorithms. In this paper, we study the performance of three memetic algorithms for the quadratic assignment problem when their parameters are tuned eithe...

A parametric model of aural tuning of acoustic pianos is presented in this paper. From a few parameters, a whole tessitura model is obtained, that can be applied to any kind of pianos. Because the tuning of piano is strongly linked to the inharmonicity of its strings, a 2-parameter model for the inharmonicity coefficient along the keyboard is introduced. Constrained by piano string design consi...

Two methods of realizing aperiodic stochastic resonance (ASR) by adding noise and tuning system parameters in a bistable system, after a scale transformation, can be compared in a real parameter space. In this space, the resonance point of ASR via adding noise denotes the extremum of a line segment, whereas the method of tuning system parameters presents the extrema of a parameter plane. We dem...

As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with...

This paper proposes a repetitive control type optimal gait generation framework by executing learning control and parameter tuning. We propose a learning optimal control method of Hamiltonian systems unifying iterative learning control (ILC) and iterative feedback tuning (IFT). It allows one to simultaneously obtain an optimal feedforward input and tuning parameter for a plant system, which min...

The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimizat...

In this paper, we provide a new method of the PID parameter tuning for time-delay systems by utilizing the fictitious reference iterative tuning (FRIT), which is a controller tuning method enabling us to obtain the desired parameter with only one-shot experimental data. Here, by relating the conventional PID controller to the internal model controller (IMC), we show that PID parameters obtained...

Using simulation, the efficacy of penalized maximum likelihood estimation of genetic covariances when employing different strategies to determine the necessary tuning parameter is investigated. It is shown that errors in estimating the tuning factor from the data using cross-validation can reduce the percentage reduction in average loss at modest sample sizes from 70% or more to 60% or less. Mi...

Although clause weighting local search algorithms have produced some of the best results on a range of challenging satisfiability (SAT) benchmarks, this performance is dependent on the careful handtuning of sensitive parameters. When such hand-tuning is not possible, clause weighting algorithms are generally outperformed by self-tuning WalkSAT-based algorithms such as AdaptNovelty and AdaptGWSA...