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. Genetic Algorithms

Initialization phase

PreviousWhat is a Genetic Algorithm?NextEvaluation Phase

Last updated 5 years ago

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The genetic algorithm begins with an initial population from which the selection process begins.

Each 'individual' - or design option - in the population is a potential solution to the overall design problem. Each individual has a unique set of features - long legs, short legs, wide top, thin top, heaviness, lightness, etc. These features are the design options' genes and are what we use to evolve our design.

Some of these features are desirable, others are not. The algorithm leverages the differences between the design options to converge to the best possible solution.

Importantly, a genetic algorithm always begins with a set of potential solutions. When doing generative design with Generative Design for Revit and Dynamo, this initial population is created randomly, based on a 'seed' of fundamental input data.

Often, a generative design algorithm is even used to create the initial population that can be fed into a genetic algorithm. In the initialization phase, it is important to consider how this initial population might vary. For example, if there is little or no variation in the population, then there is little chance that a good evolution will happen.

To ensure there is good variation in your initial population, it is important to remember **the following:

  • At least some of the genes need to have a range so that their values can change between generations.

  • The population size needs to be 'large enough'. The question of when a population is large enough is difficult to answer. Generally, it depends on the project, the number of genes, and the gene value range. A good rule of thumb is to set the population size to at least 3x the number of inputs. If the results don't start to converge to an answer, you may need to increase the population size.