@InCollection{SantiagoJśniorSale:2022:MeHyBa,
               author = "Santiago J{\'u}nior, Valdivino Alexandre de and Sales, Camila 
                         Pereira",
                title = "Metaheuristics and Hyper-heuristics Based on Evolutionary 
                         Algorithms for Software Integration Testing",
            booktitle = "Proceedings of International Joint Conference on Advances in 
                         Computational Intelligence",
            publisher = "Springer Nature Singapore",
                 year = "2022",
               editor = "Uddin, M. S. and Jamwal, P. K. and Bansal, J. C.",
                pages = "131--151",
              address = "Singapore",
             keywords = "Metaheuristics, Hyper-heuristics, Software Integration Testing, 
                         Controlled Experiment, Optimisation.",
             abstract = "Hyper-heuristics have been identified as optimisation algorithms 
                         that would have better generalisation capabilities than 
                         metaheuristics. In this article, we present a controlled 
                         experiment that evaluates four metaheuristics (evolutionary 
                         algorithms), two multi-objective (SPEA2, IBEA) and two 
                         many-objective (NSGA-III, MOMBI-II), and three selection 
                         hyper-heuristics (HRISE_R, HRISE_M, Choice Function) for the 
                         software integration testing problem. We relied on and improved 
                         our previous method which aims at generating integration test 
                         cases based on C++ source code and optimisation algorithms. 
                         Considering three different quality indicators and two types of 
                         evaluations (cross-domain and statistical analyses), results 
                         demonstrate that, for the algorithms and case studies considered 
                         in this research, classical metaheuristics, such as SPEA2 and 
                         IBEA, performed better compared to not only the most recent 
                         many-objective algorithms but also to the hyper-heuristics. This 
                         conclusion, based on empirical evidences, seems to be related to 
                         the well-known no free lunch theorems which assert that any two 
                         algorithms are equivalent when their performances are averaged 
                         across all possible problems. Hence, we claim that it is needed to 
                         carry out more rigorous experiments, in the context of 
                         optimisation, to better answer the question of generalisation in 
                         practical terms.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                  doi = "10.1007/978-981-19-0332-8_10",
                  url = "http://dx.doi.org/10.1007/978-981-19-0332-8_10",
                 isbn = "9789811903",
                label = "lattes: 5039690360728170 1 SantiagoJ{\'u}niorSale:2022:MeHyBa",
             language = "en",
           targetfile = "Paper 1_Metaheuristics_Oficial.pdf",
               volume = "1",
        urlaccessdate = "20 maio 2024"
}