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Browsing by Author "Beldek, U."

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    Developing Growing Hierarchical Structures for Decision Making
    (Ieee, 2007) Beldek, U.; Leblebicioglu, K.; 06.08. Mekatronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
    This study is about developing a hierarchical approach for decision-making problems. The development is done on a representative decision-making problem. A hierarchical decision making approach which enables fusion of decisions of previous and current levels is proposed. The agents that determine the decisions at different hierarchy levels is accomplished by utilizing a genetic algorithm.
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    Citation - Scopus: 1
    Local Decision-Making in Multiple Levels for Lottery Data Analysis
    (IFAC Secretariat, 2011) Leblebicioǧlu, K.; Beldek, U.; 59950; 06.08. Mekatronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
    Sales forecasting is a common problem in economics. Lottery sales are one of the favorite issues of sales forecasting. Sales of lottery tickets depend on many economical issues and such a problem was investigated previously (Beensock et al., 2002) where Genetic Programming is used in order to construct different agent structures that predict the number of ticket sales in Israel lottery. In each application in (Beensock et al., 2002), only a single agent is developed to predict the number of ticket sales at a present drawing. Instead we propose a Local Decision-Making model which performs the sales forecasting job in multiple levels by employing agent structures that operate locally and combine their decisions via a suitable decision fusion technique. It seems that Local Decision-Making in Multiple Levels fits well for the problem. © 2011 IFAC.
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    Strategy Creation, Decomposition and Distribution in Particle Navigation: Memory Module
    (IFAC Secretariat, 2005) Beldek, U.; Leblebicioglu, K.; 06.08. Mekatronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
    in particle navigation problem strategy development is crucial. The difficulties encountered by the particles during their navigation tasks require different approaches in problem solving. One way to overcome the difficulties is to divide the problem into simple modules and develop solutions for these modules separately. Basically, two different modules are sufficient in addition to the main body to develop a successful solver. The first module (conflict module), which is developed by genetic programming, is used when the particles are in conflict. The second module (memory module) helps the particles to escape from local regions. Copyright © 2005 IFAC.