Evolutionary algorithms (EA) are search strategies that mimic the process of natural evolution. All EA's have two fundamental strategies; a selection and a recombination strategy both o f which are known to largely influence the performance of the algorithm. The selection strategy ensures fitter individuals have a greater chance of survival and a greater participation in mating while the recombination strategy aims to inherit meaningful parent properties. In this paper a new fitness assignment scheme and a new parent selection strategy is proposed. The individuals are assigned separate fitness values in the objective a nd the constraint space unlike most EAs that use a single fitness measure for selection. The parent selection mechanism employed in the a lgorithm is both elitist and adaptive. The recombination strategy is based on a parent centric operator that explores the neighborhoods of good parents in search for better ones. In this paper we present t he results obtained b y our algorithm and compare it with the reported results on a suite of six single objective constrained test problems.