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An Evolutionary Approach To Space Layout Planning Using Genetic Algorithm

An Evolutionary Approach To Space Layout Planning Using Genetic Algorithm. By: Hoda Homayouni. Introduction to Space Layout Planning. What is Space Layout Planning? Motivation Challenges: Solving ill defined problems Addressing qualitative constraints Having creativity

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An Evolutionary Approach To Space Layout Planning Using Genetic Algorithm

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  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. Hill climbing Why Genetic Algorithm? global local

  5. Why Genetic Algorithm? • Multi-climbers

  6. 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

  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 Select individuals for the mating pool Perform crossover Perform mutation Insert offspring into the population Stop? no yes The End

  13. 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. Genetic Engineering Approach

  14. Genetic Engineering

  15. Evolving Complex Design Genes Using a Hierarchical Growth Approach

  16. Generating Units

  17. Crossover at Room Level

  18. Crossover at Room Level

  19. Crossover at Site Level

  20. Initial Living Zone Population

  21. Evolved Population

  22. Initial House population

  23. Evolved Population

  24. Discussion • More Fitness Functions • Architects Role?

  25. 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

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