This was confirmed by more than 300 sessions with the configurators, which took place during last three weeks. Parametric configurators with 4 and 18 customizable parameters (4P and 18P) where more popular than their genetic algorithms counterparts (4GA and 18GA), but genetic algorithm was a much more popular mean of driving the 37th parameters definition (37GA turned out to be more popular than 37P). I’m currently writing these findings up into a paper and some further, more in-depth analysis of the results will follow.

For the next iteration of the experiment, Federico Weber has generously offered to share his customizable table design with me. The work called Xylem is based on Voronoi diagram and will become a phenotype in Breeding Objects Experiment 2. Federico has already developed a parametric configurator for Xylem. It’s downloadable from here.

Breeding Objects Experiment 01 is my first experiment with configurators for mass customization of products over the Internet, conducted as part of PhD research at the Bartlett School of Graduate Studies. The experiment is a comparison of two types of configurators: “parametric” configurators and genetic algorithms configurators. In parametric configurators users have to specify the values of each of the parameters explicitly, while in genetic algorithms configurators they are not required to do so. Thanks to artificial selection, they can instead navigate through solution space by breeding objects, that is manually selecting parents of next generations.

The experiment is only about the patterns of interaction with the tools available, so you will not be asked to fill in any questionnaires. Once you start interaction with the tools, you will be able to navigate between them using links on top of each page.