Background:　Studies　of　functional　modules　in　a　Protein-Protein　Interaction　(PPI)　network　contribute　greatly　to　the　understanding　of　biological　mechanisms.　With　the　development　of　computing　science,　computational　approaches　have　played　an　important　role　in　detecting　functional　modules.　Results:　We　present　a　new　approach　using　multi-agent　evolution　for　detection　of　functional　modules　in　PPI　networks.　The　proposed　approach　consists　of　two　stages:　the　solution　construction　for　agents　in　a　population　and　the　evolutionary　process　of　computational　agents　in　a　lattice　environment,　where　each　agent　corresponds　to　a　candidate　solution　to　the　detection　problem　of　functional　modules　in　a　PPI　network.　First,　the　approach　utilizes　a　connection-based　encoding　scheme　to　model　an　agent,　and　employs　a　random-walk　behavior　merged　topological　characteristics　with　functional　information　to　construct　a　solution.　Next,　it　applies　several　evolutionary　operators,　i.e.,　competition,　crossover,　and　mutation,　to　realize　information　exchange　among　agents　as　well　as　solution　evolution.　Systematic　experiments　have　been　conducted　on　three　benchmark　testing　sets　of　yeast　networks.　Experimental　results　show　that　the　approach　is　more　effective　compared　to　several　other　existing　algorithms.　Conclusions:　The　algorithm　has　the　characteristics　of　outstanding　recall,　F-measure,　sensitivity　and　accuracy　while　keeping　other　competitive　performances,　so　it　can　be　applied　to　the　biological　study　which　requires　high　accuracy.