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Evo-Devo Strategies for Generative Architecture: Colour-Based Patterns in Polygon Meshes

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Parametric design in architecture is often pigeonholed by its own definition and computational complexity. This article explores the generative capacity to integrate patterns and flows analogous to evolutionary developmental biology (Evo-Devo) strategies to develop emergent proto-architecture. Through the use of coloured patterns (genotype) and the modification of polygonal meshes (phenotype), a methodological proposal is achieved that is flexible to changes and personalization, computationally efficient, and includes a wide range of typologies. Both the process and the result are oriented towards computational lightness for a future and better integration of the workflow in genetic algorithms. Flow-based programming is used to replicate genetic properties such as multifunctionality, repeatability and interchangeability. The results reinforce the biological strategies against other more computationally abstract ones and successfully execute the parallels of universal mechanisms in Evo-Devo that are present in life.
Title: Evo-Devo Strategies for Generative Architecture: Colour-Based Patterns in Polygon Meshes
Description:
Parametric design in architecture is often pigeonholed by its own definition and computational complexity.
This article explores the generative capacity to integrate patterns and flows analogous to evolutionary developmental biology (Evo-Devo) strategies to develop emergent proto-architecture.
Through the use of coloured patterns (genotype) and the modification of polygonal meshes (phenotype), a methodological proposal is achieved that is flexible to changes and personalization, computationally efficient, and includes a wide range of typologies.
Both the process and the result are oriented towards computational lightness for a future and better integration of the workflow in genetic algorithms.
Flow-based programming is used to replicate genetic properties such as multifunctionality, repeatability and interchangeability.
The results reinforce the biological strategies against other more computationally abstract ones and successfully execute the parallels of universal mechanisms in Evo-Devo that are present in life.

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