This　paper　proposes　using　neural　networks　(NN)　to　implement　a　real　coded　genetic　algorithm　(GA)　with　the　center　of　gravity　crossover　(CGX)　and　the　minimal　generation　gap　(MGG)　model.　With　all　genetic　operations　of　GA　including　selection,　crossover,　mutation　and　evaluation　implemented　with　NN　modules,　this　approach　can　realize　in　parallel　genetic　operations　on　the　whole　chromosome　to　achieve　the　maximum　parallel　realization　potential　of　the　MGG　model　of　the　GA.　At　the　same　time　expensive　hardware　for　field　programmable　gate　arrays　(FPGA)　and　the　high　speed　memory　of　hardware　for　GA　can　be　avoided.　The　performance　of　our　solution　is　validated　with　a　suite　of　benchmark　test　functions.　This　paper　suggests　that　implementing　GA　with　NN　is　a　promising　research　direction　for　greatly　reducing　the　running　time　of　GA.