Generative design has its roots in the field of computer-aided design (CAD), which dates back to the 1960s. The earliest forms of generative design involved the use of algorithms to automatically generate variations of a basic design, with the aim of finding the best possible solution to a given problem.
In the following decades, advances in computational power and artificial intelligence led to the development of more sophisticated generative design programs. In the 1990s, researchers began exploring the use of genetic algorithms to evolve designs based on a set of constraints and objectives.
In the early 2000s, the field of generative design began to see wider adoption in industries such as architecture, engineering, and manufacturing. Designers and engineers realized that by using generative algorithms, they could quickly iterate through thousands of design variations and identify the most optimized solution.
In recent years, generative design has been further enhanced through the use of machine learning algorithms, which can learn from past design data and provide even more optimized solutions. Today, generative design is used in a wide range of industries to automate the design process, increase efficiency, and reduce costs.
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