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News
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Оптимизация характеристик интеллектуальной транспортной системы с использованием генетического алгоритма вещественного кодирования на основе адаптивной мутации

Информационные технологии. 2023. Т. 29. № 3. С. 115–125.
Akopov A. S., Бекларян Л. А., Beklaryan A.

A novel real-coded genetic algorithm (FCGA-AM) that uses the proposed adaptive mutation (AM) operator is presented. The algorithm is designed to optimise the characteristics of the developed intelligent transportation system. The performance of the proposed genetic algorithm was evaluated in comparison with other methods of multicriteria heuristic optimization with the use of various test instances and well-known quality metrics for Pareto frontiers. At the next stage, the FCGA-AM was applied to find the best road safety trade-offs involving unmanned ground vehicles (UAVs) interacting with conventional ground vehicles (UTVs), pedestrians and other agents.

Research target: Mathematics Computer Science
Language: Russian
Full text
DOI
Keywords: многокритериальная оптимизацияmulti-agent systemsбезопасность дорожного движениямногоагентные системыинтеллектуальные транспортные системыIntelligent transportation systemsReal-coded genetic algorithmsгенетический алгоритм вещественного кодированияunmanned ground vehiclestraffic safetymulti-obective optimisationdigital road networksбеспилотные наземные транспортные средствацифровые дорожные сети
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