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Genetic Algorithms for Space Planning
Every architectural design process starts with the schematic design phase, wherein architects have to satisfy a collection of topological and dimensional constraints. Here, architects face a wicked problem. Some constraints contradict others, priorities are not clear and the topological constraints grow exponentially as the number of rooms in a design problem increases. In large design problems architects have to deal with a great many topological constraints and optimize the solution in compatibility with the dimensional constraints. This is a very time consuming trial-and-error task that needs more computational assistance.
Among the different computational methods that have been used in optimization problems, artificial intelligence methods have shown a potential to produce novel optimized solutions. In this thesis, an intelligent prototype is developed that generates schematic floor plan layouts in response to the architect's needs. The prototype benefits from the combinatory power of genetic algorithms and genetic engineering methods.
Related Publications
- A Genetic Algorithm Approach to Space Layout Planning Optimization (2007) [MS Theses]