Green Roof Optimization Using Multi Objective Optimization with Genetic Programming Based Artificial Neural Network
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Veer-Samara-Sihman:2024:ICIICS,
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author = "Rana {Veer Samara Sihman} and Muntader M. Almusawi and
Mohammed {Hussein Fallah} and T. Saravanan and
N Rajesh",
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title = "Green Roof Optimization Using Multi Objective
Optimization with Genetic Programming Based Artificial
Neural Network",
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booktitle = "2024 International Conference on Integrated
Intelligence and Communication Systems (ICIICS)",
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year = "2024",
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month = nov,
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keywords = "genetic algorithms, genetic programming, Heating
systems, Uncertainty, Urban areas, Artificial neural
networks, Feature extraction, Linear programming,
Optimisation, Meteorology, Load modelling, artificial
neural network, fuzzy if-then rule, green roof system,
multi objective optimisation",
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DOI = "
doi:10.1109/ICIICS63763.2024.10859609",
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abstract = "In recent years, the greenhouse gases are rising
increasingly and the extreme usage of vestige energy
resources helped in the green spaces development
including green roof systems, which is an important
focus in urban areas. In existing, green roof designs
are optimised with different methods such as Fuzzy
framework-based optimisation. However, the framework is
a single object based optimisation so, the framework
struggle to handle multi conflict objects. To overcome
this issue, Multi Objective Optimisation with Genetic
Programming based Artificial Neural Network (MOO-GPANN)
is proposed to optimise the green roof design.
Initially, the input data is taken from NYC Green Roof
Footprints (NYC-GRF) dataset and further processed into
feature extraction with fuzzy if-then rule. Then, the
optimal features are selected by using Multi Objective
Optimisation (MOO) based on ranking diversity. Finally,
the prediction done by using Genetic Programming based
Artificial Neural Network (GPANN). From the results,
the proposed MOO-GPANN model gave better results than
existing Adaptive Neuro-Fuzzy Inference System (ANFIS)
by providing results for the objective functions
including energy consumption (PMV), Heating Load (HL),
Cooling Load (CL) and comfort levels (CFL)
respectively.",
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notes = "Also known as \cite{10859609}",
- }
Genetic Programming entries for
Rana Veer Samara Sihman
Muntader M Almusawi
Mohammed Hussein Fallah
T Saravanan
N Rajesh
Citations