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职业迁徙
个人简介
My research interests (under evolution)
Evolutionary computing: applications and theory
I am particularly interested in population sizing, population diversity analysis and maintenance, saddle crossing mechanism, as well as in issues of including the domain knowledge in constraint handling, representation and operators design.
Metaheuristics for global optimization
Within that area I am working on taxonomy of metaheuristics and their relations to machine learning.
Machine learning
I have been developing a methodology to perform the nonlinear regression in an incremental way. The method is based on sequential adding nonlinear terms to the approximator using the analysis of correlation between the added term and the current residue function. Another issue we develop is the methodology of optimizing parameters of kernel functions such that the resulting SVM classifier will be small and accurate, but the optimization process is performed prior to the classifier training.
applications of optimization and machine learning techniques
The list of applications contains: automatic control of the combustion process in coal-fired power plants, decision making under uncertainty in the deregulated electricity market, support for modeling the semiconductor devices, automated DNA annotation,... (to be continued)
research in areas of shale gas and smart city
Evolutionary computing: applications and theory
I am particularly interested in population sizing, population diversity analysis and maintenance, saddle crossing mechanism, as well as in issues of including the domain knowledge in constraint handling, representation and operators design.
Metaheuristics for global optimization
Within that area I am working on taxonomy of metaheuristics and their relations to machine learning.
Machine learning
I have been developing a methodology to perform the nonlinear regression in an incremental way. The method is based on sequential adding nonlinear terms to the approximator using the analysis of correlation between the added term and the current residue function. Another issue we develop is the methodology of optimizing parameters of kernel functions such that the resulting SVM classifier will be small and accurate, but the optimization process is performed prior to the classifier training.
applications of optimization and machine learning techniques
The list of applications contains: automatic control of the combustion process in coal-fired power plants, decision making under uncertainty in the deregulated electricity market, support for modeling the semiconductor devices, automated DNA annotation,... (to be continued)
research in areas of shale gas and smart city
研究兴趣
论文共 71 篇作者统计合作学者相似作者
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Konrad Krawczyk,Jaroslaw Arabas
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANIONpp.2282-2285, (2023)
Journal of Telecommunications and Information Technologypp.65-72, (2023)
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)pp.1-8, (2022)
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