Abstract-Limited applicability of classical optimization methods influence the popularization of stochastic optimization techniques such as evolutionary algorithms (EAs). EAs are a class of probabilistic optimization techniques inspired by natural evolution process, witch belong to methods of Computational Intelligence (CI). EAs are based on concepts of natural selection and natural genetics. The basic principle of EA is searching optimal solution by processing population of individuals. This paper presents the results of simulation analysis of global optimization of benchmark function by Individually Directional Evolutionary Algorithm (IDEA) and other EAs such as Real Coded Genetic Algorithm (RCGA), elite RCGA with the one elite individual, elite RCGA with the number of elite individuals equal to population size. IDEA is a newly developed algorithm for global optimization. Main principle of IDEA is to monitor and direct the evolution of selected individuals of population to explore promising areas in the search space. The idea of IDEA is an independent evolution of individuals in current population. This process is focused on indicating correct direction of changes in the elements of solution vector. This paper presents a flowchart, selection method and genetic operators used in IDEA. Moreover, similar mechanisms and genetic operators are also discussed.
Artykuł poświęcony jest budowie i analizie inteligentnego systemu rekomendacyjnego zasobów bazującego na rozmytej mapie kognitywnej. Opracowany system pozwala wskazać zasoby strony internetowej, którymi może być zainteresowany potencjalny użytkownik. Zasoby te są określane na podstawie aktywności innych użytkowników serwisu. Bazując na zbiorze anonimowo zebranych danych historycznych opracowano rozmytą mapę kognitywną, której czynniki odpowiadają poszczególnym zasobom strony internetowej. Wagi powiązań między nimi określono na podstawie liczby użytkowników odwiedzających poszczególne zasoby.
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