In genetic science, it is well known that a specific genetic behavior is influenced by one or several other genes. There is a similar subject in Genetic Algorithm (GA), called Epistasis, which is in fact the interaction between genes. In the attached paper (which is under review), it has been claimed that in spite of what is generally supposed, GA is not an efficient optimization tool; because, its main operator, mating (crossover), cannot operate properly in Epistatic problems. On the other hand, in non-Epistatic problems, although GA can possibly lead to a correct answer, it will be an inefficient and time-consuming algorithm. In that paper, to evaluate the efficiency of optimization algorithms, several Epistatic and non-Epistatic commonly used benchmark examples were investigated. Moreover, some new test functions are also introduced for efficiency evaluation of GA which have the capability of being analyzed in both Epistatic and non-Epistatic conditions. As it is claimed, simulation results show that GA does not perform efficiently in Epistatic problems and non-Epistatic problems can be solved by less complex algorithms.