In　the　emerging　field　of　image　segmentation,　Fully　Convolutional　Networks　(FCNs)　have　recently　become　prominent.　However,　their　effectiveness　is　intimately　linked　with　the　correct　selection　and　fine-tuning　of　hyperparameters,　which　can　often　be　a　cumbersome　manual　task.　The　main　aim　of　this　study　is　to　propose　a　more　efficient,　less　labour-intensive　approach　to　hyperparameter　optimization　in　FCNs　for　segmenting　fundus　images.　To　this　end,　our　research　introduces　a　hyperparameter-optimized　Fully　Convolutional　Encoder-Decoder　Network　(FCEDN).　The　optimization　is　handled　by　a　novel　Genetic　Grey　Wolf　Optimization　(G-GWO)　algorithm.　This　algorithm　employs　the　Genetic　Algorithm　(GA)　to　generate　a　diverse　set　of　initial　positions.　It　leverages　Grey　Wolf　Optimization　(GWO)　to　fine-tune　these　positions　within　the　discrete　search　space.　Testing　on　the　Indian　Diabetic　Retinopathy　Image　Dataset　(IDRiD),　Diabetic　Retinopathy,　Hypertension,　Age-related　macular　degeneration　and　Glacuoma　ImageS　(DR-HAGIS),　and　Ocular　Disease　Intelligent　Recognition　(ODIR)　datasets　showed　that　the　G-GWO　method　outperformed　four　other　variants　of　GWO,　GA,　and　PSO-based　hyperparameter　optimization　techniques.　The　proposed　model　achieved　impressive　segmentation　results,　with　accuracy　rates　of　98.5%　for　IDRiD,　98.7%　for　DR-HAGIS,　and　98.4%,　98.8%,　and　98.5%　for　different　sub-datasets　within　ODIR.　These　results　suggest　that　the　proposed　hyperparameter-optimized　FCEDN　model,　driven　by　the　G-GWO　algorithm,　is　more　efficient　than　recent　deep-learning　models　for　image　segmentation　tasks.　It　thereby　presents　the　potential　for　increased　automation　and　accuracy　in　the　segmentation　of　fundus　images,　mitigating　the　need　for　extensive　manual　hyperparameter　adjustments.