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A Genetic Algorithm Approach To space Layout planning optimization

A Genetic Algorithm Approach To space Layout planning optimization. Hoda Homayouni. Outline. Space Layout Planning Introduction to Genetic Algorithm GASP: Structure. Space Layout Planning. Why computers?. Complexity of the large problems Shortcomings of human mind in complex problems

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A Genetic Algorithm Approach To space Layout planning optimization

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  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 global local

  8. Why Genetic Algorithm? • Multi-climbers

  9. Why Genetic Algorithm? • Genetic algorithm I am at the top Height is ... I am not at the top. My high is better! 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 modification evaluation population No Stop? Yes End

  15. Genetic Engineering

  16. GASP: Structure Genetic Algorithm Space Planner

  17. Hierarchical growth approach …. Building Layout Room1 Room 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

  28. Results

  29. Results

  30. 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

  35. Parameter Settings

  36. Parameter Settings

  37. Parameter Settings

  38. Parameter Settings

  39. Parameter Settings

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