Generative　Adversarial　Networks　(GANs)　have　brought　surprises　in　image　generation　and　processing,　particularly　excelling　in　alleviating　the　problem　of　sparse　samples　in　few-shot　learning.　Evolutionary　Generative　Adversarial　Networks　(EGANs)　aim　to　convert　the　generation　task　into　an　optimization　problem　by　integrating　multiple　loss　functions　to　minimize　the　gap　between　the　generated　distribution　and　the　data　distribution.　However,　EGANs’　heavy　reliance　on　mutation　operations　introduces　excessive　randomness,　resulting　in　unstable　generator　updates　and　affecting　the　diversity　of　generated　samples.　This　leads　to　challenges　such　as　model　collapse　and　gradient　vanishing.　In　this　paper,　inspired　by　human　grafting　mechanism,　we　propose　a　novel　Multi-branch　Evolutionary　Generative　Adversarial　Network　(ME-GAN)　based　covariance　crossover　operator,　which　includes　two　distinct　branches　–　Evolutionary　GAN　(E-GAN)　and　Conditional　Evolutionary　GAN　(CE-GAN).　Specifically,　the　E-GAN　branch　involves　a　mutation　process　to　introduce　randomness　to　the　generated　samples,　while　the　CE-GAN　branch　aims　to　provide　more　realistic　and　diversify　generated　samples　through　conditional　enhancement　and　relevant　mutation　processes.　We　propose　a　crossover　operation　that　utilizes　the　covariance　similarity　metric　to　transfer　different　feature　attributes　between　offspring　of　different　generations,　thereby　generating　a　diverse　sample.　Extensive　experiments　on　CIFAR-10,　STL-10,　and　CelebA　datasets　demonstrate　the　effectiveness　and　superiority　of　the　proposed　framework.　©　2024　Elsevier　B.V.