Neutral neighbors in bi-objective optimization: Distribution of the most promising for permutation problems

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Abstract

In multi-objective optimization approaches, considering neutral neighbors during the exploration has already proved its efficiency. The aim of this article is to go further in the comprehensibility of neutrality. In particular, we propose a definition of most promising neutral neighbors and study in details their distribution within neutral neighbors. As the correlation between objectives has an important impact on neighbors distribution, it will be studied. Three permutation problems are used as case studies and conclusions about neutrality encountered in these problems are provided.

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Kessaci-Marmion, M. E., Dhaenens, C., & Humeau, J. (2017). Neutral neighbors in bi-objective optimization: Distribution of the most promising for permutation problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10173 LNCS, pp. 344–358). Springer Verlag. https://doi.org/10.1007/978-3-319-54157-0_24

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