Abstract
In this work we evaluate the usefulness of a Path Relinking based method for generating the initial population of Multi-Objective Genetic Algorithms and evaluate its performance on the Multi-Objective Next Release Problem.The performance of the method was evaluated for the algorithms MoCell and NSGA-II, and the experimental results have shown that it is consistently superior to the random initialization method and the extreme solutions method, considering the convergence speed and the quality of the Pareto front, that was measured using the Spread and Hypervolume indexes.
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Gomes Nepomuceno Da Silva, T., Sampaio Rocha, L., Bessa Maia, J.E. (2014). An Effective Method for MOGAs Initialization to Solve the Multi-Objective Next Release Problem. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_3
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DOI: https://doi.org/10.1007/978-3-319-13650-9_3
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