The document discusses an introduction to optimization in generative design. It defines elements of optimization including parameters, objectives, and constraints. It then explains how genetic algorithms can be used for optimization, describing the process as generating generations of solutions that get better over time through steps of initialization, selection, crossover and mutation. Finally, it notes optimization can be done in Grasshopper and promises a demo.

1. Generative Design
Week 5 – Introduction to Optimization
Danil Nagy
September 10, 2019

2. Generative Design
September 10, 2019
1. Generate 2. Evaluate 3. Evolve
Elements of Generative Design

3. Generative Design
September 10, 2019
1. Generate 2. Evaluate 3. Evolve
Parametric design
and scripting
Elements of Generative Design

4. Generative Design
September 10, 2019
1. Generate 2. Evaluate 3. Evolve
Parametric design
and scripting
+ Simulation
Elements of Generative Design

5. Generative Design
September 10, 2019
1. Generate 2. Evaluate 3. Evolve
Parametric design
and scripting
+ Simulation + Optimization
Elements of Generative Design

6. Generative Design
September 10, 2019
Design space model

7. Generative Design
September 10, 2019
Design space model

8. Generative Design
September 10, 2019
Optimization

9. Generative Design
September 10, 2019
Optimization
Parameters
Constraints
Objective(s)

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September 10, 2019
Elements of optimization
1. Parameters – a set of variables that can be adjusted
• discrete / categorical - whole number
• continuous - decimal number
• permutation / ordering - whole number sequence
2. Objectives - functions representing the goals of the problem
• minimize value
• maximize value
3. Constraints - functions representing conditions that make a valid solution
1. must be equal to a certain value
2. must be smaller than a certain value
3. must be greater than a certain value

11. Generative Design
September 10, 2019
Genetic Algorithm
Generation 1 Generation 50

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September 10, 2019
Genetic Algorithm
Generations 1-50

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September 10, 2019
Travelling salesman problem (TSP)

14. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

15. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

16. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

17. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

18. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

19. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

20. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

21. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

22. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

23. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

24. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

25. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

26. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

27. Generative Design
September 10, 2019
Travelling salesman problem (TSP)

28. Generative Design
September 10, 2019
Genetic Algorithm
A Genetic Algorithm creates “generations” of solutions in such a way that the solutions get better over time
Steps:
1. Generate initial population of solutions
2. Rank solutions based on their performance in objectives and constraints
3. Generate next generation by applying elitism, crossover, and mutation to current generation
4. Repeat until termination criteria is met

29. Generative Design
September 10, 2019
1. Initialization 2. Selection 3. Crossover 4. Mutation
Genetic algorithm
Genetic Algorithm

30. Generative Design
September 10, 2019https://colidescope.com/discover/
Discover

31. Generative Design
September 10, 2019
Optimization in Grasshopper

32. Generative Design
September 10, 2019
Demo