A centrality based genetic algorithm for the graph burning problem

Nazeri, Mehdi; Mollahosseini, Ali; Izadi, Iman

doi:10.1016/j.asoc.2023.110493

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B Genetic Algorithms1

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2024

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“…That approach creates the algorithms by applying some selections and mutation operations. It can handle numerous scheduling problems and requirements [2]. The roulette wheel selection is frequently employed method for choosing chromosomes in genetic algorithms.…”

Section: B Genetic Algorithmsmentioning

confidence: 99%

Development of Environment for Generating Personalized Schedules

Vytak,

Semchyshyn

2023

Theoretical and Applied Aspects of Device Development on Microcontrollers and FPGAs 2023

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Section: B Genetic Algorithmsmentioning

confidence: 99%

Development of Environment for Generating Personalized Schedules

Vytak,

Semchyshyn

2023

Theoretical and Applied Aspects of Device Development on Microcontrollers and FPGAs 2023

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A Machine Learning Based Framework for Brine-Gas Interfacial Tension Prediction: Implications for H2, CH4 and CO2 Geo-Storage

Pan,

Song,

Yin

et al. 2024

Day 2 Wed, May 08, 2024

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Brine-gas interfacial tension (γ) is an important parameter to determine fluid dynamics, trapping and distributions at pore-scale, thus influencing gas (H2, CH4 and CO2) geo-storage (GGS) capacity and security at reservoir-scale. However, γ is a complex function of pressure, temperature, ionic strength, gas type and mole fraction, thus time-consuming to measure experimentally and challenging to predict theoretically. Therefore herein, a genetic algorithm-based automatic machine learning and symbolic regression (GA-AutoML-SR) framework was developed to predict γ systematically under GGS conditions. In addition, the sensitivity of γ to all influencing factors was analyzed. The prediction results have shown that: the GA-AutoML-SR model prediction accuracy was high with the coefficient of determination (R2) of 0.994 and 0.978 for the training and testing sets, respectively;a quantitative mathematical correlation was derived as a function of pressure, temperature, ionic strength, gas type and mole fraction, withR2= 0.72;the most dominant influencing factor for γ was identified as pressure. These insights will promote the energy transition, balance energy supply-demand and reduce carbon emissions.

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A centrality based genetic algorithm for the graph burning problem

Cited by 2 publications

References 14 publications

Development of Environment for Generating Personalized Schedules

Development of Environment for Generating Personalized Schedules

A Machine Learning Based Framework for Brine-Gas Interfacial Tension Prediction: Implications for H2, CH4 and CO2 Geo-Storage

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