Evolving digital 3D models using interactive genetic algorithm

Sammanfattning: The search space of digital 3D models designs is vast and can be hard to navigate. In this study, a system that evolves digital 3D models using an interactive genetic algorithm (IGA) was constructed in order to aid this process. The goal of the study was to investigate how such a system can be constructed in order to aid the design space exploration of digital 3D models.  The system is integrated with the 3D creation suite Blender and uses its Python API to programmatically edit models and generate images of the results, which are displayed on a web page where users can rate the results to evolve the model. The proposed system exposes all settings for the genetic algorithm, which includes population size, mutation rate, crossover algorithm, selection algorithm and more. Furthermore, the settings can be modified throughout the evolutionary process as well as the ability to rewind the algorithm and go back to previous generations in order to give more control in the progression of the algorithm. The script based nature of the proposed system is powerful but not practical for people without programming experience. For widespread adoption of IGAs as an exploratory design aid tool, it would help if the IGA is directly integrated into the design software being used in order to make it easier to use and reduce user fatigue.