Effectively　estimating　numerous　free　parameters　in　large-scale　biophysical　neural　network　models　based　on　sparse　biological　functional　data　is　a　formidable　task.　In　this　paper,　we　propose　a　simple　yet　efficient　parameters　optimization　method　called　the　iterative　fine-grained　genetic　algorithm　(IFGA)　for　rapid　inference　of　the　connection　weights　in　the　large-scale　biophysical　V1　models　released　by　Allen　Institute　(referred　to　as　the　Allen　V1　model)　[2].　IFGA　fully　leverages　the　characteristics　of　the　connectivity　in　Allen　V1　model　to　reduce　the　dimensionality　of　parameters　to　be　optimized.　It　also　utilizes　the　relationship　between　optimization　objectives　and　weight　changes,　as　well　as　the　discrepancies　between　model　simulation　data　and　expected　data,　to　design　an　ingenious　search　strategy　for　each　parameter,　significantly　improving　the　search　efficiency　in　high-dimensional　parameter　space.　A　wealth　of　experimental　results　demonstrates　that　the　proposed　IFGA　not　only　achieves　better　convergence　of　the　Allen　V1　model　with　the　given　mice　data　but　also　accomplishes　faster　convergence.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.