Generative Design Primer
  • Welcome
  • Introduction to Generative Design
    • Computational Design
    • Generative Design
      • What is Generative Design?
      • Why should I use Generative Design?
      • What goes into a Generative Design Process?
        • Anatomy of each stage
      • Examples of Generative Design
        • MaRs Innovation District of Toronto
        • Furniture Design
        • A Further Analogy
      • Anatomy of a Good Generative Design Process
    • Visual Programming
    • Dynamo
    • Generative Design for Revit and Dynamo
  • Deeper Dive to Generative Design
    • Algorithms
      • What are Algorithms?
      • Generators
      • Evaluators
      • Solvers
    • Optioneering
    • Optimization
      • What is Optimization?
      • Objective Function
      • Constraints
      • Data
      • Defining Goals
    • Genetic Algorithms
      • What is a Genetic Algorithm?
      • Initialization phase
      • Evaluation Phase
      • Selection Phase
      • Crossover Phase
      • Mutation Phase
    • Other Techniques
    • Genetic Algorithm Q&A
  • Hello Generative Design for Revit and Dynamo!
    • Installing Generative Design
    • Setting up a Graph for Generative Design
    • Running Generative Design
    • Visualizing Results in Generative Design
    • Refinery Toolkit
      • Installing the Refinery Toolkit from the Dynamo Package Manager
      • Using the Refinery Toolkit
    • Space Analysis for Dynamo
      • Installing the Space Analysis for Dynamo package from the Dynamo Package Manager
      • Using the Space Analysis Package
    • Using Revit alongside Generative Design
      • Using Data from Revit
      • Remember Node Inputs
      • How to Test Revit Data Capture
      • Detailed Example Workflow
      • Sharing Logic and Results
      • Current Limitations
      • Accessing Generative Design Directly From Revit
  • Sample Workflows
    • Getting Started Workflows
      • Highest Point of a Surface
      • Minimum Volume and Maximum Surface
    • Architectural Workflows
      • Building Mass Generator
      • Building Positioning based on Solar Analysis
      • Office Layout
      • Grid Object Placement in a Room
      • Entourage Placement Exploration
    • MEP Workflows
      • Distributing Spotlights in an Office Space
    • Structural Workflows
    • BIM Workflows
      • Placement of views on sheets
    • Community Examples
      • Guidelines
      • List Of Examples
  • Generative Design in Your Office
    • What Generative Design Can Be Used For?
    • What Generative Design Can’t Be Used For?
    • How to Convince Senior Stakeholders of Using Generative Design?
    • The Role of a Generative Designer
    • Hiring a Generative Designer
  • Next Steps
    • Machine Learning
      • What is Machine Learning?
      • Is Generative Design Machine Learning?
      • Can Machine Learning and Generative Design Work Together?
  • Appendix
    • Glossary
    • Reference Material
    • Need Professional Help?
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  1. Deeper Dive to Generative Design
  2. Optimization

Data

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Last updated 5 years ago

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Data is the fundamental 'stuff' that you feed into your optimization model - the inputs and outputs that the objective function and constraints use to produce design solutions.

In design, data could be the density or price of construction materials, how many hours of sunlight a room can expect to receive in a day, or any goals that are important to your design exploration that you can define mathematically. In finance, data could be the assets you can buy or sell, and their prices; or, in the aerospace industry, data could be the unit weights and costs of carrying a certain kind of fuel.

The optimization model in the generative design toolkit takes this data and uses it to maximize or minimize values as specified by the designer.

Real-world optimization problems are invariably solved algorithmically and there are often many algorithms that can solve the same problem. The most common algorithm used in Generative Design for architectural and engineering workflows is called the 'genetic algorithm'. We will cover this later on.