1. An Evolutionary Approach To Space Layout Planning Using Genetic Algorithm By: Hoda Homayouni

2. Introduction to Space Layout Planning What is Space Layout Planning? Motivation Challenges: –Solving ill defined problems –Addressing qualitative constraints –Having creativity –Compatibility with architects

3. Introduction to Genetic Algorithm Computer Algorithm that resides on principles of genetic and evolution.

4. 4 Why Genetic Algorithm? Hill climbing local global

5. Why Genetic Algorithm? Multi-climbers

6. 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

7. Why Genetic Algorithm? Genetic algorithm few microseconds after

8. Encoding Chromosomes The chromosome should in some way contain information about solution which it represents

9. Crossover Crossover selects genes from parent chromosomes and creates a new offspring

10. Mutation This is to prevent falling all solutions in population into a local optimum of solved problem

11. Fitness Function Fitness function is evaluation function, that determines what solutions are better than others. Fitness is computed for each individual. Fitness function is application depended.

12. Algorithmic Phases Initialize the population Initialize the population Select individuals for the mating pool Perform crossover Insert offspring into the population The End Perform mutation yes no no Stop?

13. Genetic Engineering Approach An object can be described by the location of units and can be ‘grown’ by locating a required number of such units, one at a time in sequence.

14. Genetic Engineering

15. Evolving Complex Design Genes Using a Hierarchical Growth Approach

16. Generating Units

17. Crossover at Room Level

20. Crossover at Site Level

21. Initial Living Zone Population

22. Evolved Population

23. Initial House population

24. Evolved Population

25. Discussion More Fitness Functions Architects Role?

26. References Rosenman, M.A. (1997). The Generation of form using evolutionary approach”. Evolutionary algorithms in Engineering Applications. Springer, 1997. Rosenman, M.A. and Gero, J.S. (1999) Evolutionary designs by generating useful complex gene structures. Evolutionary Design by Computers, Morgan Kaufmann, San Francisco, pp.345-364. http://galeb.etf.bg.ac.yu/~vm/GenAlgo.ppt