How does the good old Genetic Algorithm fare at real world optimization?

Genetic Algorithms (GAs) have been studied for more than three decades now. Their application in optimization problems is well understood and significant amount of research has gone into the development of efficient GA operators. Besides GAs, a number of other Evolutionary Algorithms (EAs) and their performance-enhancing variations have been proposed. However, this upgraded performance is often achieved at the undesirable cost of introducing additional user-defined parameters. In an attempt to put forward a case for GA even when a plethora of other EAs are available, we present the results obtained by using a Real-Coded, Elite preserving GA on the Real World optimization problems of IEEE CEC - 2011. Based on our preliminary investigations, we would like to stress that the current work shall help bring forth the need to take a step back to re-assess the applicability of basic GAs to practical optimization before yet another Bio-inspired algorithm is introduced.