Explain the theoretical model of production and development of architectural plans in the interaction of machine learning algorithms and genetics

Subject Areas : Architecture and urbanism

reza babakhani ¹ , azadeh Shahcheraghi ² , hossein zabihi ³

1 - Researcher PhD of Architecture, Department of Architecture, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - PhD of Architecture, Associate Professor, Department of Architecture, Science and Research Branch, Islamic Azad University, Tehran, Iran. *(Correspondence Author)
3 - PhD of Urban Development, Associate Professor, Department of Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Received: 2021-02-03 Accepted : 2021-03-08 Published : 2022-05-22

Keywords: Genetic algorithm, automatic plan production, Machine learning, plan production,

Abstract :

Background and Objective: The aim of this study is to explain to the theoretical model in order to find a new solution for the production and development of spatial arrangement of architectural plans based on interactive and integrated methods with the help of machine learning and genetic algorithms. Evolutionary algorithms alone are not effective, but machine learning algorithms can learn plans and form the basis of practical models that can develop and generate new samples through the use of genetic algorithms.Material and Methodology: In this regard, the combined research method includes library studies, collecting raw data, reviewing case samples, and using computational formulas as objective and penalty functions.Findings: Studies show that the genetic algorithm does not have the ability to store memory and on the other hand, the basis of its calculations is jumping and random action that this process is not effective in the production of architectural plans alone and research.Discussion and Conclusion:  findings show that the algorithm Machine learning, due to its exemplary structure, can store and recognize examples, and the genetic algorithm, which is a searchable and scalable algorithm, can produce more examples of architectural plans each time based on the proposed mathematical model.

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