Table 2. Genetic algorithm (GA) and summary of the final merging statistics for concanavalin A.
In a GA, each iteration, or GA generation, results in a series of possible individuals for best approximating a function, and the GA population refers to the complete set or pool of these generated individuals after a given iteration. Each target also has a user-specified weight associated with it. All targets are then summed to produce a single fitness score for each group in the individual. For additional details, refer to Zander et al. (2016 ▸).
| No. of partial data sets collected | 298 |
| No. of partial data sets integrated | 180 |
| No. of partial data sets selected | 116 |
| GA population size (individuals) | 50 |
| GA generations | 400 |
| GA R target weight | 100 |
| GA I target weight | 1000 |
| GA CC1/2 weight | 300 |
| GA groups | 3 |
| Resolution range | 42.83–1.929 (1.998–1.929) |
| Total No. of reflections† | 9145, 21675, 619871 |
| No. of unique reflections† | 379, 2389, 34104 |
| Completeness† (%) | 99.2, 94.7, 99.6 |
| Multiplicity† | 24.1, 9.1, 18.2 |
| R value† (%) | 9.20, 43.2, 14.4 |
| R meas † (%) | 9.4, 45.8, 14.8 |
| 〈I/σ(I)〉† | 50.41, 4.93, 18.89 |
| SigAno† | 2.287, 0784, 1.061 |
| CC1/2 † | 99.8, 94.1, 99.9 |
†
The values reported are for the inner shell, for the outer shell and overall, respectively.