Feature　selection　is　a　widely　used　technique　to　remove　the　undesirable,　noisy　and　inaccurate　information　from　raw　input　dataset　while　maintaining　the　accuracy　and　efficiency　of　classifier.　Tremendous　researches　have　explored　the　feasibility　of　metaheuristic　search　algorithms　(MSAs)　such　as　African　Vultures　Optimization　Algorithm　(AVOA)　to　solve　feature　selection　problem.　Similar　with　many　original　MSAs,　the　conventional　initialization　scheme　of　AVOA　has　undesirable　drawbacks　that　can　lead　to　entrapment　of　local　optima,　especially　when　dealing　with　complex　dataset.　In　this　paper,　a　new　variant　known　as　Chaotic　African　Vultures　Optimization　Algorithm　(CAVOA)　is　proposed　to　solve　feature　selection　problem　with　enhanced　classification　accuracy　by　incorporating　the　chaotic　map　concept　into　the　initialization　scheme.　Twelve　datasets　obtained　from　UCI　Machine　Learning　Repository　are　used　to　investigate　the　capability　of　CAVOA　in　feature　selection　and　compared　with　four　other　peer　algorithms.　Simulation　results　show　that　CAVOA　can　produce　the　best　classification　accuracies　and　lowest　feature　numbers　in　most　datasets.　©　The　2023　International　Conference　on　Artificial　Life　and　Robotics　(ICAROB2023).