Towards a Precise and Mathematical Fractalesque Architecture

Abstract

This paper reviews a design process in the context of algorithmic architecture design for establishing a scale-invariant and rigorous self-similar motif(s) that can be applied generally to any design problem. An architect (author) defines a genetic algorithm (GA) using a population of design variants iterated over multiple generations. Exemplars are selected based on their fractal dimension (FD) along with the architect and fit to solve a real-world architectural problem. The algorithm is coded in Python and Ruby with an interface in SketchUp. The architect is able to modify exemplars and iterate them as many times as required in the GA until an acceptable solution is achieved. Solutions are critiqued by a jury of professional architects regarding their fractal qualities. Results show a fractal motif that is not strictly self-similar and not strictly scale-invariant. Discussion is focused here on the philosophical implications of this research in terms of better defining a fractalesque architecture. The case for a more precise and mathematical fractalesque architecture is discussed concluding that further development of the algorithmic design process is necessary to clarify the value of such a tool.