Is stochastic ranking really better than Feasibility Rules for constraint handling in Evolutionary Algorithms?

Evolutionary algorithms have been widely used to solve difficult constrained optimization problems. However, evolutionary algorithms are intrinsically unconstrained optimization techniques. Constraint handling is mostly incorporated additionally and its choice has great bearing on the quality of the solution. Stochastic ranking was introduced as an improvement over feasibility rules for handling constraints in evolutionary optimization. It is widely believed that stochastic ranking is currently the best-known technique for handling constraints. However, a fair comparative study has never been attempted in the literature, where by the performance of both the constraint handling technique is compared on the same evolutionary algorithm. This paper fairly compares the performance of both the constraint handling techniques on the same evolutionary algorithm over a set of parameters like feasibility rate, successful run, success rate and success performance in addition to objective function value and number of function evaluations. The results put a question mark over the belief that feasibility rules are worse than stochastic ranking.