29 Fig.2.8 Development of wing pigment patterns.In the pictures are subgroups of picture wing species of adiastola and primaeva/attigua [Online] Available at http://www.bio.ilstu. edu/edwards/hawaiiandrosophila/index. shtml[Accessed September 02, 2011]
This field of genomics is often concerned only with living organisms since only they have genomes. However this phenomenon can be potentially tested within the field of architecture considering buildings as genetic organisms, which are an output of series of methods that achieve their ultimate form. This is the base of the concept of genetic algorithm for architectural design. The genetic algorithm depends on the methods of randomness and to search for a fittest individual of a population. Thus this project is aimed to develop and use genetic algorithm as a means of searching for an optimum design solution for addressing various architectural problems. Also developing a better understanding of genetic algorithms is of prime focus. The attempt would be to find not only a single optimal solution but rather a range of fit options which can be visualized and evaluated by a range of criterias. The most basic steps for Genetic algorithms are the generation of population and its fitness function.
The primary reason of the fitness function would be to find the optimal solution. For that reason it is important to know the phenomenon or quality to be optimised. These can be anything that are related to architectural problems like cost , solar radiation ,shadows ,daylight quality , acoustic quality etc. Basically any parameter that influence the execution of design could be a fitness function. However there must be a mean to simulate the fitness, and the results must be quantifiable. Thus the fitness should be an output value which represents the performance of the individual which can be evaluated.
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