“Evolutionary problem solving mimics the theory of evolution employing the same trial-and-error methods that nature uses in order to arrive at an optimized result.”  One way to obtain a computational evolutionary result is to use an Evolutionary Solver (Galapagos) combined with a Graphic Algorithm Editor (Grasshopper) that makes the process easier and more efficient. The result is the production of optimized parameters (maximized and/or minimized) by controlling the population and iteration number.

The plugin was used for the Self Sufficient Buildings seminar. The project is located in the Mojave Desert, California and consists of a 100 m diameter dome which includes a series of interior suspended shelters.  The skin and structure of the Dome were analyzed separately. Both solar and radiation analysis were done to define the skin panels that receive a higher radiation percentage.

The interior suspended shelters were located randomly to organize them inside the dome. After the location of the shelters, I included solar position vectors at defined dates and times (Heliotrope) to have an varied range or results. When the Evolutionary Solver was combined with this Solar Vectors the result was an optimization (maximization) in the location of the spheres.