
Model your ancestry with optimization algorithms. Adjust populations, compute genetic distances, and visualize results with treegrams and similarity maps.
Supports Vahaduo, G25 Dodecanomics, and raw coordinate formats
Works like the distance slider — higher = more precise but slower. Controls how many optimization attempts the algorithm runs to find the best ancestry fit.
Populations
18
Iterations
10,000
Coordinates
Demo Sample
| # | Population | Distance | Fit |
|---|---|---|---|
| 1 | Rusyn | 0.000000 | Excellent |
| 2 | Hutsul | 0.007273 | Excellent |
| 3 | Polish Podkarpackie | 0.015480 | Excellent |
| 4 | Dolinyan | 0.028196 | Great |
| 5 | Ukrainian Zakarpattia | 0.029533 | Great |
| 6 | Lemko Poland | 0.030228 | Great |
| 7 | Slovakia Lemko | 0.036876 | Great |
| 8 | Polish Lesser Poland | 0.036876 | Great |
| 9 | White Croat | 0.038110 | Great |
| 10 | Boyko | 0.040898 | Great |
| 11 | Romanian Maramureș | 0.047476 | Good |
| 12 | Romania Transylvania | 0.057276 | Good |
| 13 | Košice | 0.168617 | Distant |
| 14 | Polish-Lithuanian Commonwealth | 0.184590 | Distant |
| 15 | Pannonian Rusyn | 0.186819 | Distant |
| 16 | Ukrainian Ivano-Frankivsk | 0.187985 | Distant |
| 17 | Austria-Hungary | 0.192856 | Distant |
| 18 | Romani | 0.213353 | Distant |
18
Populations Analyzed
—
Best Fit Distance
10,000
Iterations Completed
Demo Sample
Sample Name
Our simulator uses advanced optimization algorithms to model your genetic ancestry against reference populations
Paste your G25 coordinates in any format — Vahaduo, G25 Dodecanomics, or raw numbers
Select reference populations, distance metric, and iteration count
Monte Carlo algorithm optimizes population proportions to find best fit
View distances, treegrams, and geographic similarity maps
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