1. A GENETIC ALGORITHM APPROACH TO SPACE LAYOUT PLANNING OPTIMIZATION Hoda Homayouni

2. Outline  Space Layout Planning  Introduction to Genetic Algorithm  GASP: Structure

3. Space Layout Planning

4. Why computers?  Complexity of the large problems  Shortcomings of human mind in complex problems  Excellent rational and search ability of computers

5. Challenges  Ill defined problems  Qualitative constraints  Usability for architects

6. Introduction to Genetic Algorithm

7. Why Genetic Algorithm??  Hill Climbing local global

8. Why Genetic Algorithm?  Multi-climbers

9. Why Genetic Algorithm?  Genetic algorithm I am not at the top. My high is better! I am at the top Height is... I will continue

10. Why Genetic Algorithm?  Genetic algorithm few microseconds after

11. Components of a GA Survival of the fittest Encoding technique (gene, chromosome) Initialization procedure (creation) Genetic operators (mutation, crossover) Evaluation function (environment) Selection of parents (reproduction)

12. Genetic Operators Crossover

13. Genetic Operators Mutation Parent Offspring

14. GA Phases reproduction population evaluation modification End Stop? Yes No

15. Genetic Engineering

16. Genetic Algorithm Space Planner GASP: Structure

17. Hierarchical growth approach …. Building Layout Room1Room 2 Room N

18. Room Level Operations Crossover

19. Room Level Operations Fitness function  Area fitness  Perimeter fitness  Concavity fitness  Proportion fitness

20. Creation of rooms in GASP

21. Building Level Operations Initialization

22. Building Level Operations Crossover

23. Building Level Operations Evolving Genes

24. Building Level Operations Mutation

25. Building Level Operations Fitness function  Area fitness  Perimeter fitness  Proportion fitness  Adjacency fitness

26. Creation of a building layout in GASP

27. Results

31. Future Work  More fitness functions  Interactive environment  Multi-story layout problems  More heuristic Methods

32. The End

33. References  [1] http://galeb.etf.bg.ac.yu/~vm/GenAlgo.ppt  [2] http://web.umr.edu/~ercal/387/slides/GATutorial.ppt

34. Parameter Settings