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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  • Randomize
  • Optimize
  • Cross Product
  • Like This

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  1. Deeper Dive to Generative Design
  2. Algorithms

Solvers

PreviousEvaluatorsNextOptioneering

Last updated 5 years ago

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'Solvers' are a tools that can automatically run a script many times that contains both generators and evaluators.

Solvers typically require inputs to be very specific. Often, the greatest challenge is defining your problem in a way that a solver can understand.

To take a simple example, your phone’s calculator is a solver for addition, subtraction, and division – but it only works if you punch things in correctly.

A solver can use different methods to process these scripts in different ways. The methods currently available in Generative Design are listed below.

Randomize

'Randomize' generates a specified number of design options by randomly assigning a value to each of the input parameters. This process is used for optioneering processes in Generative Design.

Optimize

'Optimize' is the method for doing an optimization run with Generative Design. During an optimization run, Generative Design will develop the design based on the evaluator's outputs.

The optimization process works by creating multiple 'generations' (or iterations) of a design, where each iteration will use the input configuration from previous generation to optimize the new design options.

Cross Product

'Cross Product' lets you explore the entire design space of your design by combining each step of every parameter with the other parameters available.

Like This

'Like This' will make Generative Design apply slight variations to your current input configuration. Using this method, you can explore different variations of a design that you already like.