Designing with parameters, objectives, and constraints

  • by Danil
  • Course level: Intermediate


In this session, we will dive deeper into optimization and cover the four basic components of the genetic algorithm: generation, selection, crossover, and mutation. We will also review three standard parameter types we can use to control our generative models and discuss the difference between objectives and constraints when specifying our design goals.

In the hands-on demo, I’ll show you how to use Discover’s special sequence input type to solve the classic Travelling Salesman Problem. Starting with a map of cities, we will create a Grasshopper model that tests various possible routes, and then use Discover to quickly find the best possible solution.

Topics for this course

3 Lessons02h 09m 18s

Designing with parameters, objectives, and constraints

Evaluating performance with multiple objectives00:37:11
Demo: Parameter types, objectives vs. constraints00:47:37

About the instructors

Danil Nagy is a designer, developer, and entrepreneur focusing on applications of computational design and automation for the building industries. His expertise includes computational geometry, digital fabrication, simulation, optimization, machine learning, and data visualization. Danil teaches at the Graduate School of Architecture, Planning and Preservation (GSAPP) at Columbia University in New York, where his courses focus on architectural visualization, generative design, and applications of artificial intelligence. Danil was formerly a Principal Research Scientist at Autodesk Research. He is the founder of Colidescope, a consultancy focused on bringing digital transformation tools to the Architecture, Engineering, and Construction (AEC) industries.
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16 Courses

132 students

Material Includes

  • Over two hours of on-demand video
  • Downloadable demo files to follow along with video tutorials