WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
Browse
3 results
Search Results
Now showing 1 - 3 of 3
Article Citation - Scopus: 1Cost Effective Localization in Distributed Sensory Networks(Elsevier Ltd., 2011) Coşkun, Anıl; Sevil, Hakkı Erhan; Özdemir, SerhanThe most important mechanism to occur in biological distributed sensory networks (DSNs) is called lateral inhibition, (LI). LI relies on one simple principle. Each sensor strives to suppress its neighbors in proportion to its own excitation. In this study, LI mechanism is exploited to localize the unknown position of a light source that illuminated the photosensitive sensory network containing high and low quality sensors. Each photosensitive sensor was then calibrated to accurately read the distance to the light source. A series of experiments were conducted employing both quality sensors. Low quality array was allowed to take advantage of LI, whereas the high quality one was not. Results showed that the lateral inhibition mechanism increased the sensitivity of inferior quality sensors, giving the ability to make the localization as sensitive as high quality sensors do. This suggests that the networks with multitude of sensors could be made cost-effective, were these sensory networks equipped with LI.Article Citation - WoS: 2Citation - Scopus: 2Trait-based heterogeneous populations plus (TbHP+) genetic algorithm(Elsevier Ltd., 2009) Tayfur, Gökmen; Sevil, Hakkı Erhan; Gezgin, Erkin; Özdemir, SerhanThis study developed a variant of genetic algorithm (GA) model called the trait-based heterogeneous populations plus (TbHP+). The developed TbHP+ model employs a memory concept in the form of immunity and instinct to provide the populations with a more efficient guidance. Also, it has an ability to vary the number of individuals during the search process, thus allowing an automatic determination of the size of the population based on the individual qualities such as character fitness and credit for immunity. The algorithm was tested against the classical GA model in convergence and minimum error performance. For this purpose, 5 different mathematical functions from the literature were employed. The selected functions have different topological characteristics, ranging from simple convex curves with 2 variables to complex trigonometric ones having several hilly shapes with more than 2 variables. The developed model and the classical GA model were applied to finding the global minima of the functions. The comparison of the results revealed that the developed TbHP+ model outperformed the classical GA in faster convergence and minimum errors, which may be explained by the adaptive nature of the new paradigm.Conference Object Citation - Scopus: 1The Effects of Bias, Population Migration and Credit Assignment in Optimizing Trait-Based Heterogeneous Populations(CSREA Press, 2005) Gezgin, Erkin; Sevil, Hakkı Erhan; Özdemir, SerhanPopulation based search algorithms are becoming the mainstay in nonlinear problems with discontinuous search domains. The generic name of genetic algorithms (GAs) basicly applies to all population based methods. GAs have spawned many versions to suit new applications. Some of these alterations have reached such points that the algorithms may no longer be called GAs. One similar study may be found in [1], in which a perturbation based search algorithm was proposed, called Responsive Perturbation Algorithm (RPA). In a later work [2], instead of a population of homogenous individuals, as is the case for generic GAs, a population of heterogeneous individuals has been set to compete. Replacing the set of winner parents, the fittest individual is made the parent to yield offspring. The current work is now called, with the supplements, trait-based heterogeneous populations plus (TbHP+). Credit assignment and bias concepts in the form of immunity and instinct has been added to provide the populations with a more efficient guidance. Simulations were made through an RBF neural network training, as it was carried out in earlier works, mentioned above, for comparison. Results were prsented at the end as network testing errors which showed further improvement with TbHP+.