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Evo-Devo Algorithms: Gene-Regulation for Digital Architecture.

Diego Navarro-Mateu1, Ana Cocho-Bermejo2

  • 1School of Architecture, Universitat Internacional de Catalunya, Carrer Immaculada 22, 08017 Barcelona, Spain. navarro@uic.es.

Biomimetics (Basel, Switzerland)
|August 16, 2019
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel approach combining genetics algorithms with evolutionary developmental biology to create flexible, diverse visual-algorithmic architectures. This method prioritizes simple, hierarchical structures for enhanced design generation.

Area of Science:

  • Computational design
  • Evolutionary developmental biology
  • Algorithmic architecture

Background:

  • Current visual-algorithmic architectures are limited by fixed, gradient-based parameters, hindering flexible topology and parametric modeling.
  • Recent advances in developmental biology highlight universal mechanisms in species' shape conditioning, linking genetics, embryology, and evolution.

Purpose of the Study:

  • To propose a new dual discipline integrating evolutionary developmental biology and genetics algorithms.
  • To develop a flexible data-flow for generating diverse visual-algorithmic designs.

Main Methods:

  • Convergence of genetics algorithms with simulated features from evolutionary developmental biology.
  • Development of a simple, flexible data-flow structure for commands, data, and polygonal geometry.
Keywords:
evolutionary developmentgenetic algorithmsproto-architecturevisual programming

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  • Case study using visual-algorithmic software to test the hypothesis.

    • Main Results:

    • Demonstration of generating significant diversity through a simplified, flexible algorithmic structure.
    • Validation of the hypothesis that simpler, hierarchical approaches enhance design space and emergence.
    • Successful integration of biological developmental principles into computational design.

    Conclusions:

    • A simpler, more flexible approach prioritizing hierarchical levels is sufficient for greater emergence in design.
    • The proposed convergence offers a powerful strategy for generative design and architectural innovation.
    • Evolutionary developmental biology principles can inform and enhance computational design methodologies.