Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning

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@Article{kalyuzhnaya:2021:Entropy,

• author = "Anna V. Kalyuzhnaya and Nikolay O. Nikitin and Alexander Hvatov and Mikhail Maslyaev and Mikhail Yachmenkov and Alexander Boukhanovsky",
• title = "Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning",
• journal = "Entropy",
• year = "2021",
• volume = "23",
• number = "1",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1099-4300",
• URL = "https://www.mdpi.com/1099-4300/23/1/28",
• DOI = "doi:10.3390/e23010028",
• abstract = "In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models design and co-design, the generalised formulation of the modelling workflow is proposed. A parallelized evolutionary learning approach for the identification of model structure is described for the equation-based model and composite machine learning models. Moreover, the involvement of the performance models in the design process is analysed. A set of experiments with various models and computational resources is conducted to verify different aspects of the proposed approach.",
• notes = "also known as \cite{e23010028}",

}

Genetic Programming entries for Anna V Kalyuzhnaya Nikolay O Nikitin Alexander Hvatov Mikhail Maslyaev Mikhail Yachmenkov Alexander Boukhanovsky

Citations