Optimisation has always had a bit of an identity crisis in creative disciplines. On one hand, it promises efficiency, performance, and precision. On the other hand, it often seems at odds with the emotional and intuitive qualities we associate with art, architecture, and design - the poetics of space, form, and experience.

But what if optimisation didn’t have to mean compromise?

What if, instead of reducing architecture to numbers, we could invite the algorithm into our creative process - not to take control, but to co-evolve with us?

Architects have long relied on drawings, models, and words to communicate their ideas. But today, much of architecture unfolds in a space that resists easy visualisation or narration: the algorithmic layer. As computation becomes central to design, a new challenge emerges - how do we talk about, draw, and critique code-driven processes?

This post explores the art of narrating algorithmic design - not just for fellow coders or technologists, but for a broader audience who still expects architecture to tell a story.

In the age of digital design, architecture is no longer drawn - it’s computed. Behind the curves, grids, and surfaces we see in contemporary buildings lies a hidden layer: code. But while code has become an indispensable design material, can it also be a medium for beauty?

This post explores the aesthetic dimension of computation - not just as a means to an end, but as a generator of form, feeling, and even poetry.

If ecological architecture is about designing with nature - not just borrowing from its aesthetics but engaging its intelligence - then computational tools are becoming the translator. They allow us to simulate, test, and refine designs in ways that align more closely with the principles of ecological systems: adaptability, feedback, interdependence, and performance.

Before anything is built, these tools allow us to ask: How will this form behave? How does it respond to its environment? Can it evolve, adapt, and regenerate?

Today, computational design environments - like Grasshopper for Rhino - enable architects and designers to create complex systems of parameters and relationships. You’re not just drawing lines or shapes; you’re defining rules and behaviours that generate form. This is the foundation of parametric design. 

But while parametric tools let us define ‘how’ things behave, they don’t help us decide ‘what’ is good. That’s where optimisation tools come in. And when it comes to subjective or hard-to-quantify goals, this is where IGAs truly shine.

As I’ve touched on in earlier posts, evolutionary algorithms are inspired by the logic of natural selection: selecting, combining, and mutating solutions over generations to solve problems. Since the 1980s, Genetic Algorithms (GAs) have been widely used for optimisation and classification tasks, excelling particularly in fields where goals can be clearly defined and measured with numerical fitness values. 

But here’s the catch: that clarity breaks down when we shift into creative domains like design, architecture, art, or music. These are spaces where subjective judgment, such as taste, intuition, and aesthetic feeling, plays a crucial role. And that’s where traditional GAs reach their limit. How do you assign a fitness score to a beautiful sketch? Or rank the poetic resonance or the appealing proportion of a form? You can’t. Not objectively, anyway.