Mechanical Engineering / Makina Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/4129
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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+.Conference Object Citation - WoS: 1Citation - Scopus: 6Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues(Taylor and Francis Ltd., 2002) Akkurt, Sedat; Özdemir, Serhan; Tayfur, GökmenA multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.