Interplay Between Optimal Selection Scheme, Selection Criterion, and Discrete Rate Adaptation in Opportunistic Wireless Systems

2013 ◽  
Vol 61 (7) ◽  
pp. 2735-2745 ◽  
Author(s):  
N. B. Mehta ◽  
R. Talak ◽  
A. T. Suresh
2004 ◽  
Vol 12 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Jörn Dunkel ◽  
Lutz Schimansky-Geier ◽  
Werner Ebeling

In this paper two different evolutionary strategies are tested by means of harmonic landscapes. Both strategies are based on ensembles of searchers, spreading over the search space according to laws inspired by nature. The main difference between the two prototypes is given by the underlying selection mechanism, governing the increase or decrease of the local population of searchers in certain regions of the search space. More precisely, we compare a thermodynamic strategy, which is based on a physically motivated local selection criterion, with a biologically motivated strategy, which features a global selection scheme (i.e., global coupling of the searchers). Confining ourselves to a special class of initial conditions, we show that, in the simple case of harmonic test potentials, both strategies possess particular analytical solutions of the same type. By means of these special solutions, the velocities of the two strategies can be compared exactly. In the last part of the paper, we extend the scope of our discussion to a mixed strategy, combining local and global selection.


Author(s):  
Tengyue Li ◽  
Simon Fong

Diabetes has become a prevalent metabolic disease nowadays, affecting patients of all age groups and large populations around the world. Early detection would facilitate early treatment that helps the prognosis. In the literature of computational intelligence and medical care communities, different techniques have been proposed in predicting diabetes based on the historical records of related symptoms. The researchers share a common goal of improving the accuracy of a diabetes prediction model. In addition to the model induction algorithms, feature selection is a significant approach in retaining only the relevant attributes for the sake of building a quality prediction model later. In this article, a novel and simple feature selection criterion called Coefficient of Variation (CV) is proposed as a filter-based feature selection scheme. By following the CV method, attributes that have a data dispersion too low are disqualified from the model construction process. Thereby the attributes which are factors leading to poor model accuracy are discarded. The computation of CV is simple, hence enabling an efficient feature selection process. Computer simulation experiments by using the Prima Indian diabetes dataset is used to compare the performance of CV with other traditional feature selection methods. Superior results by CV are observed.


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