Genetic　algorithm　(GA)　is　a　nature-inspired　algorithm　to　produce　best　possible　solution　by　selecting　the　fittest　individual　from　a　pool　of　possible　solutions.　Like　most　of　the　optimization　techniques,　the　GA　can　also　stuck　in　the　local　optima,　producing　a　suboptimal　solution.　This　work　presents　a　novel　metaheuristic　optimizer　named　as　the　binary　chaotic　genetic　algorithm　(BCGA)　to　improve　the　GA　performance.　The　chaotic　maps　are　applied　to　the　initial　population,　and　the　reproduction　operations　follow.　To　demonstrate　its　utility,　the　proposed　BCGA　is　applied　to　a　feature　selection　task　from　an　affective　database,　namely　AMIGOS　(A　Dataset　for　Affect,　Personality　and　Mood　Research　on　Individuals　and　Groups)　and　two　healthcare　datasets　having　large　feature　space.　Performance　of　the　BCGA　is　compared　with　the　traditional　GA　and　two　state-of-the-art　feature　selection　methods.　The　comparison　is　made　based　on　classification　accuracy　and　the　number　of　selected　features.　Experimental　results　suggest　promising　capability　of　BCGA　to　find　the　optimal　subset　of　features　that　achieves　better　fitness　values.　The　obtained　results　also　suggest　that　the　chaotic　maps,　especially　sinusoidal　chaotic　map,　perform　better　as　compared　to　other　maps　in　enhancing　the　performance　of　raw　GA.　The　proposed　approach　obtains,　on　average,　a　fitness　value　twice　as　better　than　the　one　achieved　through　the　raw　GA　in　the　identification　of　the　seven　classes　of　emotions.