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In search-based structural testing, metaheuristic search techniques have been frequently used to automate the test data generation. In Genetic Algorithms (GAs) for example, test data are rewarded on the basis of an objective function that represents generally the number of statements or branches covered. However, owing to the wide diversity of possible test data values, it is hard to find the set of test data that can satisfy a specific coverage criterion. In this paper, we introduce the use ofdoi:10.1109/sbst.2015.17 dblp:conf/icse/BoussaaBSB15 fatcat:bzl6yf33gbhlbiflz5wb25qwbi