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Evaluating a Multi-objective Hyper-Heuristic for the Integration and Test Order Problem

Multi-objective evolutionary algorithms (MOEAs) have been successfully applied for solving different software engineering problems. However, adapting and configuring these algorithms for a specific problem can demand significant effort from software engineers. Therefore, to help in this task, a hyper-heuristic, named HITO (Hyper-heuristic for the Integration and Test Order problem) was proposed to adaptively select search operators during the optimization process. HITO was successfully applied using NSGA-II for solving the integration and test order problem. HITO can use two hyper-heuristic selection methods: Choice Function and Multi-armed Bandit. However, a hypotheses behind this study is that HITO does not depend of NSGA-II and can be used with other MOEAs. To this aim, this paper presents results from evaluation experiments comparing the performance of HITO using two different MOEAs: NSGA-II and SPEA2. The results show that HITO is able to outperform both MOEAs.