Heuristic algorithms for decoding phase of non-adaptive group testing

Group testing is a longstanding problem with numerous applications in social life, it plays an important role in quickly performing sample tests and identifying and localizing the defect items on a large scale, in conditions where the number of tests is limited. The step of analyzing the pooling test matrix to predict the desired results requires certain technical probability calculations and combinatorial reasoning. In this study, we would like to propose an approach that uses a group of evolutionary algorithms to determine the patient’s situation based on information from performed tests and experiments with data samples on different materials to compare. Eventually, the paper evaluates the effectiveness of the aforementioned algorithms.