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Vladimir Stanovov, Shakhnaz Akhmedova, Aleksei Vakhnin, Evgenii Sopov, Eugene Semenkin and Michael Affenzeller
In this study, the modification of the quantum multi-swarm optimization algorithm is proposed for dynamic optimization problems. The modification implies using the search operators from differential evolution algorithm with a certain probability within p...
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Vladimir Stanovov, Shakhnaz Akhmedova and Eugene Semenkin
Parameter adaptation is one of the key research fields in the area of evolutionary computation. In this study, the application of neuroevolution of augmented topologies to design efficient parameter adaptation techniques for differential evolution is con...
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Shakhnaz Akhmedova, Vladimir Stanovov and Yukihiro Kamiya
In this study, a new approach for novelty and anomaly detection, called HPFuzzNDA, is introduced. It is similar to the Possibilistic Fuzzy multi-class Novelty Detector (PFuzzND), which was originally developed for data streams. Both algorithms initially ...
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Vladimir Stanovov, Shakhnaz Akhmedova and Eugene Semenkin
In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely the difference-based mutation. This operator uses the differences between individuals in the population to perform more efficient search for optima...
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Shakhnaz Akhmedova, Vladimir Stanovov, Danil Erokhin and Olga Semenkina
In this study, a new modification of the meta-heuristic approach called Co-Operation of Biology-Related Algorithms (COBRA) is proposed. Originally the COBRA approach was based on a fuzzy logic controller and used for solving real-parameter optimization p...
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Vladimir Stanovov, Shakhnaz Akhmedova and Yukihiro Kamiya
In this study, a new voting procedure for combining the fuzzy logic based classifiers and other classifiers called confidence-based voting is proposed. This method combines two classifiers, namely the fuzzy classification system, and for the cases when t...
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Vladimir Stanovov, Shakhnaz Akhmedova and Eugene Semenkin
In this study, a new parameter control scheme is proposed for the differential evolution algorithm. The developed linear bias reduction scheme controls the Lehmer mean parameter value depending on the optimization stage, allowing the algorithm to improve...
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