▁introdu ces ▁a ▁gen etic ▁encoding ▁called ▁common ▁gen etic ▁encoding ▁( C GE ) ▁that ▁handles ▁both ▁direct ▁and ▁indirect ▁encoding ▁of ▁neural ▁networks ▁within ▁the ▁same ▁theoretical ▁framework . ▁The ▁encoding ▁has ▁important ▁properties ▁that ▁makes ▁it ▁suitable ▁for ▁evol ving ▁neural ▁networks : ▁▁ ▁It ▁is ▁complete ▁in ▁that ▁it ▁is ▁able ▁to ▁represent ▁all ▁types ▁of ▁valid ▁phen ot ype ▁networks . ▁ ▁It ▁is ▁closed , ▁i . e . ▁every ▁valid ▁gen ot ype ▁represents ▁a ▁valid ▁phen ot ype . ▁( Sim ilar ly , ▁the ▁encoding ▁is ▁closed ▁under ▁gen etic ▁operators ▁such ▁as ▁struct ural ▁mut ation ▁and ▁c ros so ver .) ▁ ▁These ▁properties ▁have ▁been ▁formally ▁proven ▁in ▁. ▁ ▁For ▁evol ving ▁the ▁structure ▁and ▁weights ▁of ▁neural ▁networks , ▁an ▁evolution ary ▁process ▁is ▁used , ▁where ▁the ▁expl oration ▁of ▁structures ▁is ▁executed ▁at ▁a ▁larger ▁times c ale ▁( struct ural ▁expl oration ), ▁and ▁the ▁explo itation ▁of ▁existing ▁structures ▁is ▁done ▁at ▁a ▁smaller ▁times c ale ▁( struct ural ▁explo itation ). ▁In ▁the ▁struct ural ▁expl oration ▁phase , ▁new ▁neural ▁structures ▁are ▁developed ▁by ▁gradually ▁adding ▁new ▁structures ▁to ▁an ▁initially ▁minimal ▁network ▁that ▁is ▁used ▁as ▁a ▁starting ▁point . ▁In ▁the ▁struct ural ▁explo itation ▁phase , ▁the ▁weights ▁of ▁the ▁currently ▁available ▁structures ▁are ▁optimized ▁using ▁an ▁evolution ▁strategy . ▁ ▁Performance ▁ ▁E ANT ▁has ▁been ▁tested ▁on ▁some ▁benchmark ▁problems ▁such ▁as ▁the ▁double - pole ▁bal