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GENE_ARCH: An Evolution-Based Generative Design System for Sustainable Architecture

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 4200)

Abstract

GENE_ARCH is an evolution-based Generative Design System that uses adaptation to shape energy-efficient and sustainable architectural solutions. The system applies goal-oriented design, combining a Genetic Algorithm (GA) as the search engine, with DOE2.1E building simulation software as the evaluation module. The GA can work either as a standard GA or as a Pareto GA, for multicriteria optimization. In order to provide a full view of the capacities of the software, different applications are discussed: 1) Standard GA: testing of the software; 2) Standard GA: incorporation of architecture design intentions, using a building by architect Alvaro Siza; 3) Pareto GA: choice of construction materials, considering cost, building energy use, and embodied energy; 4) Pareto GA: application to Siza’s building; 5) Standard GA: Shape generation with single objective function; 6) Pareto GA: shape generation with multicriteria; 7) Pareto GA: application to an urban and housing context. Overall conclusions from the different applications are discussed.

Keywords

  • Genetic Algorithm
  • Pareto Front
  • Multiobjective Optimization
  • Pareto Frontier
  • Standard Genetic Algorithm

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/11888598_12
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References

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© 2006 Springer-Verlag Berlin Heidelberg

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Caldas, L. (2006). GENE_ARCH: An Evolution-Based Generative Design System for Sustainable Architecture. In: Smith, I.F.C. (eds) Intelligent Computing in Engineering and Architecture. EG-ICE 2006. Lecture Notes in Computer Science(), vol 4200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11888598_12

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  • DOI: https://doi.org/10.1007/11888598_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46246-0

  • Online ISBN: 978-3-540-46247-7

  • eBook Packages: Computer ScienceComputer Science (R0)