Is a comparison of results meaningful from the inexact replications of computational experiments?

The main objective of this paper is to correct the unreasonable and inaccurate criticism to our previous experiments using Teaching-Learning-Based Optimization algorithm and to quantify the amount of error that may arise due to incorrect counting of fitness evaluations. It is shown that inexact experiment replication should be avoided in comparisons between meta-heuristic algorithms whenever possible. Otherwise, an inexact replication and margin of error should be explicitly reported.