We　propose　a　differential　evolution-based　algorithm　for　constrained　global　optimization.　Although　differential　evolution　has　been　used　as　the　underlying　global　solver,　central　to　our　approach　is　the　penalty　function　that　we　introduce.　The　adaptive　nature　of　the　penalty　function　makes　the　results　of　the　algorithm　mostly　insensitive　to　low　values　of　the　penalty　parameter.　We　have　also　demonstrated　both　empirically　and　theoretically　that　the　high　value　of　the　penalty　parameter　is　detrimental　to　convergence,　specially　for　functions　with　multiple　local　minimizers.　Hence,　the　penalty　function　can　dispense　with　the　penalty　parameter.　We　have　extensively　tested　our　penalty　function-based　DE　algorithm　on　a　set　of　24　benchmark　test　problems.　Results　obtained　are　compared　with　those　of　some　recent　algorithms.