Measuring efficiency in DEA by differential evolution algorithm

In Data Envelopment Analysis (DEA) models, for measuring the relative efficiency of Decision Making Units (DMUs), for a large dataset with many inputs/outputs would need to have a long time with a huge computer. This paper proposed and developed the Differential evolution (DE) for DEA. DE requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Since the operators have important roles on the fitness of the algorithms, all the operators and parameters are calibrated by means of the Taguchi experimental design in order to improve their performances.