Evolutionary Algorithms and the Biological Analogy
The discourse around evolutionary algorithms (EAs) in architecture often leans heavily on the assumption that these algorithms replicate nature. At first glance, it’s an appealing notion - designs emerging shaped by unseen forces. But such framing risks misunderstanding what these algorithms actually do.
In reality, evolutionary algorithms are mathematical models that simulate specific behaviours observed in biological systems. They are inspired by the principles of natural evolution, such as adaptation, natural selection, crossover and mutation, but they aren’t evolutionary in a biological sense.
However, in architectural discourse, terms like ‘genetic’ or ‘biological architecture’ are frequently employed to rhetorically borrow the authority of science. However, relying on analogy to establish credibility does not constitute a sound argument. For example, while fractals are often referred to as the ‘geometry of nature’, they merely mimic certain patterns found in natural systems rather than replicating their underlying processes.
The analogy between biology and computation can be powerful as a metaphor, a poetic lens through which to reimagine design. But it’s important to remember: metaphor is not a mechanism. The true value of evolutionary tools in architecture lies not in their claim to mimic life, but in their ability to open new modes of thinking where emergence, adaptation, and feedback become central to the creative process. So, when do analogies serve design well?
When used carefully, analogy can be one of the most powerful tools in a designer’s cognitive repertoire. Analogies don’t prove a concept, but they illuminate one. They allow us to see a familiar idea through an unfamiliar lens, or vice versa. They help us cross disciplinary boundaries, mapping insights from one domain into another. In this way, analogies don’t validate a method, but they expand the imagination of what a method could become. One distinguished method here is the Evolutionary Algorithms (EAs).
What are Evolutionary Algorithms?
First developed in the 1950s and 60s by scientists inspired by biology, evolutionary algorithms are a class of algorithms that simulate the adaptive logic of natural evolution. At their core, they borrow key principles from nature, such as selection, mutation, and recombination, and apply them to solving complex problems that require not just one answer, but a range of potential solutions.
Rather than following a linear path to a single ‘optimal’ answer, evolutionary algorithms work by evolving a population of possible solutions over time. Just like in nature, only the fittest survive. Each solution is evaluated using a fitness function - a measure that determines how well it meets the design criteria or performance goal.
Through cycles of selection, reproduction, and variation, better solutions gradually emerge - not because they are perfect, but because they are better suited to the context or constraints they are evolving within. These algorithms are especially powerful when navigating large, complex, or poorly defined design spaces - making them a natural fit for creative disciplines like architecture, where multiple competing objectives often need to be balanced. Their bio-inspired mechanics have found a natural place in creative fields like architecture.
But here’s the question: if they draw inspiration from nature, how are they different from biomimicry?
Biomimicry vs. Bio-inspiration
The question is: how do we distinguish biomimicry from bio-inspiration? Biomimicry attempts a literal replication, say, copying the form of a leaf or the geometry of a shell. Bio-inspiration, on the other hand, draws from principles: adaptability, redundancy, self-organisation, etc. It is less about copying nature’s outputs and more about understanding its processes. For example, the Eastgate Centre in Harare famously drew inspiration from termite mounds, not by mimicking their form, but by applying principles of passive thermoregulation. The analogy wasn’t formal or aesthetic, but it was performative.
In the next post, I’ll explore how co-evolutionary design leverages analogy not just as a metaphor, but as a methodology - where designers and systems evolve together through mutual feedback.
