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. Introduction to Generative Design
  2. Generative Design
  3. Examples of Generative Design

A Further Analogy

PreviousFurniture DesignNextAnatomy of a Good Generative Design Process

Last updated 5 years ago

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Let’s now look at generative design through the lens of something most of us do on a daily basis: finding the quickest commute route to work.

In this example, let's say you’re looking to go from Brooklyn to Manhattan. You go to your favorite route-comparison website and ask it to find you the quickest route between these two locations.

Above: The Citymapper website showing possible routes between Brooklyn and Manhattan, while also considering multiple modes of transportation.

To help illustrate this analogy, let's make a table comparing the expected activities when searching for the quickest commute route and what their equivalent would be in a generative design process.

Map activity

Equivalent in generative design process

Person (you) sets first parameter: go from Brooklyn to Manhattan

Stage: generate Step: set generation parameters

Computer generates possible routes from Brooklyn to Manhattan, taking into consideration all the available transportation modes, their operating status and set routes

Stage: generate Step: run generation algorithm

Person sets goals: quickest route

Stage: rank

Step: define ranking objectives

Computer evaluates each of the identified routes based on your goals

Stage: rank

Step: run ranking

Computer attempts to solve your goals and returns the list of routes, putting most suitable ones first

Stage: evolve

Step: run evolution

Person evaluates the list of best options and makes a choice more efficiently than if they had to do it themselves

Stage: explore

Step: evaluate options

Person chooses preferred route and sends travel instructions to their phone

Stage: integrate

Step: integrate preferred option

It's important to note that, because the computer knows about multiple modes of transport (walk, cycle, bus, train, etc.), it can combine them to find the best option. This means the goal we set the computer can have a big effect on the routes that are generated.

For example, if we specified that we wanted to travel by car, or that we needed step-free access, the resulting routes would be completely different.

Above: Citymapper website showing routes that have step-free access.

In this example, we can see that the generated routes take longer than in the first example, as the new goal is to have 'step-free access' instead of 'quickest commute'.

Though this example may be stretching the imagination (as a computer doesn't generate new transport modes, it just generates new routes using existing modes), this analogy demonstrates similar steps as those found in a generative design workflow.