Meta-heuristics　are　of　significant　interest　to　decision-makers　due　to　the　capability　of　finding　good　solutions　for　complex　problems　within　a　reasonable　amount　of　computational　time.　These　methods　are　further　known　to　perform　according　to　how　their　algorithm-specific　parameters　are　set.　As　most　practitioners　aim　for　an　off-the-shelf　approach　when　using　meta-heuristics,　they　require　an　easy　applicable　strategy　to　calibrate　its　parameters　and　use　it.　This　chapter　addresses　the　so-called　Parameter　Setting　Problem　(PSP)　and　presents　new　developments　for　the　Instance-specific　Parameter　Tuning　Strategy　(IPTS).　The　IPTS　presented　only　requires　the　end　user　to　specify　its　preference　regarding　the　trade-off　between　running　time　and　solution　quality　by　setting　one　parameter　p　(0　≤　p　≤1),　and　automatically　returns　a　good　set　of　algorithm-specific　parameter　values　for　each　individual　instance　based　on　the　calculation　of　a　set　of　problem　instance　characteristics.　The　IPTS　does　not　require　any　modification　of　the　particular　meta-heuristic　being　used.　It　aims　to　combine　advantages　of　the　Parameter　Tuning　Strategy　(PTS)　and　the　Parameter　Control　Strategy　(PCS),　the　two　major　approaches　to　the　PSP.　The　chapter　outlines　the　advantages　of　an　IPTS　and　shows　in　more　detail　two　ways　in　which　an　IPTS　can　be　designed.　The　first　design　approach　requires　expert-based　knowledge　of　the　meta-heuristic＇s　performance　in　relation　to　the　problem　at　hand.　The　second,　automated　approach　does　not　require　explicit　knowledge　of　the　metaheuristic　used.　Both　designs　use　a　fuzzy　logic　system　to　obtain　parameter　values.　Results　are　presented　for　an　IPTS　designed　to　solve　instances　of　the　Travelling　Salesman　Problem　(TSP)　with　the　meta-heuristic　Guided　Local　Search　(GLS).　©　2013,　IGI　Global.