What is generative design?

Generative design is an innovative approach to the creative process that uses computational algorithms to explore and generate multiple solutions to a design problem. Unlike traditional methods, where the designer creates a model based on their own experience and creativity, in generative design, the designer defines the rules and constraints of the problem, and the software generates numerous variations and possibilities that meet those parameters.
This process relies on programming and the use of algorithms to simulate the behaviour and evolution of designs. Algorithms may include techniques such as topology optimisation, evolutionary algorithms, and machine learning. These methods allow the software to produce designs that may not be intuitively obvious to humans but are innovative and optimised according to predefined criteria.

A fundamental aspect of generative design is its ability to handle complexity and explore an extensive range of design options within a short timeframe. For example, in the field of graphic design, it can be used to generate shapes and structures that respond to both the designer's predefined settings and the interaction or input provided by the final audience, creating variable results depending on user behaviour.

Generative design also encourages creativity by allowing designers to explore more alternatives than would be possible manually. The iterative process fosters innovation, as each new generation of designs builds on the previous one, refining and improving solutions as it progresses.

An algorithm is a set of defined instructions used to perform a task, making it a key element in any program or operating system. Based on these instructions, algorithms analyse data and information to study user behaviour.
To carry out this task, all types of algorithms share specific characteristics:

• Accuracy: A clear order of execution is established.
• Completeness: It presents all possible solutions to the problem.
• Finiteness: it must have a limited number of steps.
• Clarity: it must be defined and understandable.
• Generalisation: it applies to several problems within a given context.

In the case of generative design, algorithms generate various design options based on constraints, providing designers with a wide range of possibilities. Additionally, algorithms work in continuous iterations to improve the design, creating a product that best fits the conditions.

Although the exact date of origin of generative design is unknown, the textile industry is considered one of its precursors. In this sector, the goal was to optimise manufacturing through pattern creation, automating weaving machines so that the process no longer had to be repeated manually.

Due to its technological nature, the evolution of generative design has relied on combining design with fields such as computer science and mathematics, along with advances in algorithmic design methodologies and user interface development. The rise of artificial intelligence has also played a key role in its growth.

While the textile industry marked the early steps of generative design, it has since been applied across many sectors. In aerospace and automotive engineering, for example, it helps reduce weight and increase the strength of components—ultimately saving fuel and improving structural design by focusing on areas of weakness.

Another case is architecture, where generative design facilitates decision-making. Since it provides multiple results based on established parameters, architects can quickly and efficiently evaluate the best project option.

Generative design is considered a key tool in sectors that offer products or services to the public. Lars Hesellgren summarised this idea by stating: "Generative design does not aim to create a building. It aims to design a system that builds a building."

So how can this help in a cultural environment? Generative design proves highly useful for teaching multimedia projects, programming concepts, and digital art. It allows the creative phases of making a film, composing a song, or writing a book to be approached from an entirely new angle.

What about in a museum? As with the disciplines mentioned above, generative design enables institutions to engage with their audiences by incorporating them into the creative process. This system also enables endless iterations with diverse results, allowing museums to discover new ideas and concepts to build their programming around. In short, you define the parameters, and generative design offers the best options based on your instructions.

Thanks to algorithms' ability to generate infinite results, audience participation can become essential. In this way, the exhibition becomes an interactive experience where the user plays a key role in the outcome.

At Misterio Studio, we've also worked with generative design to explore its creative potential to the fullest. One example is Era's Muse: STEAM Journey, an international project that utilises programming languages such as Processing and p5.js. Through this algorithmic discipline, we created a website and an exhibition with a comprehensive visual universe that evolved in response to the user's interaction with the platform.