ARGORITHM | MACHINE LEARNING
[ Multi-Agent Aggregation Based on Training ] These test are the result of optimization of agent training. The test is to make a simple pavilion. These results are the most optimized aggregation based on a family of ten set each generation.tions are also be created using a bounding box and given limit of 1000 components. Each component is colour coded in grey scale to differentiate each group. - Different Results of Multi Agent Iteration ID - 1 iteration - 0
iteration - 4
- numbers of component
- numbers of component
0
0
clay wood
13
6
clay wood
iteration - 8
iteration - 12
- numbers of component
- numbers of component
23
16
clay wood
34
27
clay wood
iteration - 16
iteration - 20
- numbers of component
- numbers of component
47
40
clay wood
219 BARTLETT | ARCHITECTURAL DESIGN
59
52
clay wood