Discovering Knowledge through Highly Interactive Information Based Systems

[EN] The new Internet era has increased a production of digital data. The mankind had an easy way to the knowledge access never before, but at the same time the rapidly increasing rate of new data, the ease of duplication and transmission of these data across the Net, the new available channels for information dissemination, the large amounts of historical data, questionable quality of the existing data and so on are issues for information overload that causes more difficult to make decision using the right data. Soft-computing techniques for decision support systems and business intelligent systems present pretty interesting and necessary solutions for data management and supporting decision-making processes, but the last step at the decision chain is usually supported by a human agent that has to process the system outcomes in form of reports or visualizations. These kinds of information representations are not enough to make decisions because of behind them could be hidden information patterns that are not obvious for automatic data processing and humans must interact with these data representation in order to discover knowledge. According to this, the current special issue is devoted to present nine experiences that combine visualization and visual analytics techniques, data mining methods, intelligent recommendation agents, user centered evaluation and usability patterns, etc. in interactive systems as a key issue for knowledge discovering in advanced and emerging information systems.[ES] La nueva era de Internet ha aumentado la producción de datos digitales. Nunca nates la humanidad ha tenido una manera más fácil el acceso a los conocimientos, pero al mismo tiempo el rápido aumento de la tasa de nuevos datos, la facilidad de duplicación y transmisión de estos datos a través de la red, los nuevos canales disponibles para la difusión de información, las grandes cantidades de los datos históricos, cuestionable calidad de los datos existentes y así sucesivamente, son temas de la sobrecarga de información que hace más difícil tomar decisiones con la información correcta. Técnicas de Soft-computing para los sistemas de apoyo a las decisiones y sistemas inteligentes de negocios presentan soluciones muy interesantes y necesarias para la gestión de datos y procesos de apoyo a la toma de decisiones, pero el último paso en la cadena de decisiones suele ser apoyados por un agente humano que tiene que procesar los resultados del sistema de en forma de informes o visualizaciones. Este tipo de representaciones de información no son suficientes para tomar decisiones debido detrás de ellos podrían ser patrones de información ocultos que no son obvios para el procesamiento automático de datos y los seres humanos deben interactuar con estos representación de datos con el fin de descubrir el conocimiento. De acuerdo con esto, el presente número especial está dedicado a nueve experiencias actuales que combinan técnicas de visualización y de análisis visual, métodos de minería de datos, agentes de recomendación inteligentes y evaluación centrada en el usuario y patrones de usabilidad, etc. En sistemas interactivos como un tema clave para el descubrimiento de conocimiento en los sistemas de información avanzados y emergentes

A hybrid kidney algorithm strategy for combinatorial interaction testing problem

Combinatorial Interaction Testing (CIT) generates a sampled test case set (Final Test Suite (FTS)) instead of all possible test cases. Generating the FTS with the optimum size is a computational optimization problem (COP) as well as a Non-deterministic Polynomial hard (NP-hard) problem. Recent studies have implemented hybrid metaheuristic algorithms as the basis for CIT strategy. However, the existing hybrid metaheuristic-based CIT strategies generate a competitive FTS size, there is no single CIT strategy can overcome others existing in all cases. In addition, the hybrid metaheuristic-based CIT strategies require more execution time than their own original algorithm-based strategies. Kidney Algorithm (KA) is a recent metaheuristic algorithm and has high efficiency and performance in solving different optimization problems against most of the state-of-the-art of metaheuristic algorithms. However, KA has limitations in the exploitation and exploration processes as well as the balancing control process is needed to be improved. These shortages cause KA to fail easily into the local optimum. This study proposes a low-level hybridization of KA with the mutation operator and improve the filtration process in KA to form a recently Hybrid Kidney Algorithm (HKA). HKA addresses the limitations in KA by improving the algorithm's exploration and exploitation processes by hybridizing KA with mutation operator, and improve the balancing control process by enhancing the filtration process in KA. HKA improves the efficiency in terms of generating an optimum FTS size and enhances the performance in terms of the execution time. HKA has been adopted into the CIT strategy as HKA based CIT Strategy (HKAS) to generate the most optimum FTS size. The results of HKAS shows that HKAS can generate the optimum FTS size in more than 67% of the benchmarking experiments as well as contributes by 34 new optimum size of FTS. HKAS also has better efficiency and performance than KAS. HKAS is the first hybrid metaheuristic-based CIT strategy that generates an optimum FTS size with less execution time than the original algorithm-based CIT strategy. Apart from supporting different CIT features: uniform/VS CIT, IOR CIT as well as the interaction strength up to 6, this study also introduces another recently variant of KA which are Improved KA (IKA) and Mutation KA (MKA) as well as new CIT strategies which are IKA-based (IKAS) and MKA-based (MKAS)

An Adaptive Flex-Deluge Approach to University Exam Timetabling

This paper presents a new methodology for university exam timetabling problems, which draws upon earlier work on the Great Deluge metaheuristic. The new method introduces a “flexible” acceptance condition. Even a simple variant of this technique (with fixed flexibility) outperforms the original Great Deluge algorithm. Moreover, it enables a run-time adaptation of an acceptance condition for each particular move. We investigate the adaptive mechanism where the algorithm accepts the movement of exams in a way that is dependent upon the difficulty of assigning that exam. The overall motivation is to encourage the exploration of a wider region of the search space. We present an analysis of the results of our tests of this technique on two international collections of benchmark exam timetabling problems. We show that 9 of 16 solutions in the first collection and 11 of 12 solutions in the second collection produced by our technique have a higher level of quality than previously published methodologies. </jats:p

Güncel en iyileme algoritmalarının paralel ve birlikte uygulamaları ve performans analizleri

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.En iyileme yöntemleri yapılan işin en iyi yapılmasını sağlamak için kullanılırlar. Bu tekniklerin kullanılmasındaki temel hedef her zaman için en iyi çözümleri yakalayabilmektir. Uygunluk veya hata değeri tanımlanabilen her sistemin en iyi çözümünün elde edilmesinde en iyileme algoritmaları kullanılabilir. Sadece ait oldukları problemlere özgü olmaları ve yüksek hesaplama maliyeti içermeleri gibi sebepler nedeniyle mevcut geleneksel en iyileme algoritmalarının kullanımı çok sayıda parametre içeren gerçek dünya problemlerinin çözümünde bazen yeterli olmayabilir. Bu gibi durumlarda daha az işlem ile daha kısa sürede en iyi çözüme yakınsayabilen meta-sezgisel yöntemlerin kullanımı daha makul çözümler olarak karşımıza çıkmaktadır. Son 20 yıl içerisinde doğadan ilham alınarak çok sayıda meta-sezgisel en iyileme algoritması geliştirilmiştir. Buna paralel olarak bazı araştırmacılar mevcut algoritmalar üzerinde birtakım iyileştirmeler yapmışlar, bazıları da birden fazla algoritmayı bir arada kullanarak performansı daha yüksek melez yöntemler elde etmişler ve daha sonra bu yöntemleri kullanarak gerçek dünya problemlerine en iyi çözümler üretmişlerdir. Bu tez çalışmasında sistem kimliklendirme süreci, yapay sinir ağı eğitimi, sempozyum katılımcı listelerinin düzenlenmesi, slab kesme uzunluklarının planlanması gibi gerçek dünyaya ait problemlere birer en iyileme problemi olarak yaklaşılmış, seçilen güncel ve yaygın meta-sezgisel algoritmalar kullanılarak geleneksel yöntemlerin çözümleri ile rekabet edebilen çözümler üretilmiştir. Ayrıca, karar ağacı tasarım süreci hem kombinatoryal hem de nümerik en iyilemeleri içeren bir problem olarak ele alınmış, olası karar ağacı tasarımları arasında sistematik arama yapan yeni bir yöntem ile karar ağacı tasarımı gerçekleştirilmiştir. Önerilen yöntemle elde edilen test sonuçlarının aynı veri setinin kullanıldığı daha önceki karar ağacı çalışmaları ile elde edilen sonuçlardan daha iyi olduğu görülmüştür. Son olarak, yapay arı koloni ve göçmen kuşlar en iyileme algoritmaları kullanılarak yeni modifiye, melez ve paralel çalışma sistematikleri önerilmiştir. Önerilen yöntemlerin performans testlerinden elde edilen sonuçlar, onların daha iyi keşif ve yakınsama yeteneklerine sahip olduklarını ortaya koymuştur.Optimization methods are employed in order to make a job in an optimal way. The main aim of their usage is to get an optimal solution in every execution. Optimization algorithms can be applied to find optimal solutions for the systems whose fitness or error calculations can be defined. Sometimes, existing conventional optimization algorithms may be insufficient for the real world problems having many parameters because of the reason that they are problem specific and have higher calculation costs. Since metaheuristic algorithms can find near optimal solutions with less calculations requiring lower time, their usages seem more feasible for these cases. Within the past 20 years, so many metaheuristic algorithms which are inspired by the nature have been developed by researchers. In parallel to these studies, while some of the researchers were working on some enhancements for existing algorithms, some of them were working on their hybrid forms. Then, they tried to find more optimal solutions for real world problems by using these new enhanced and hybrid algorithms. In this dissertation study, some real world problems such as system identification process, artificial neural network training, preparation of symposium attendee lists, scheduling slab cutting lengths etc. are thought to be optimization problems. Some competitive solutions with respect to solutions of the conventional methods are generated to these real world problems by using some recent and common metaheuristic algorithms. In addition, thinking the decision tree construction process as a problem including both numerical and combinatorial optimizations, a novel decision tree construction method which makes a systematic search among possible decision tree designs is proposed to get optimal decision tree. It is seen that the results obtained by proposed method are better than those of previous studies using same data set. Finally, some modified, hybrid and parallel running strategies using artificial bee colony and migrating birds optimization algorithms are proposed. It is observed from the performance test results that proposed strategies have better exploration and exploitation capabilities