Neutral Neighbors in Bi-objective Optimization: Distribution of the Most Promising for Permutation Problems

Marie-Eléonore Kessaci 1, 2, 3 Clarisse Dhaenens 1, 2, 3 Jérémie Humeau 4, 5
1 ORKAD - Operational Research, Knowledge And Data
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
3 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
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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Conference papers
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https://hal.archives-ouvertes.fr/hal-01566859
Contributor : Marie-Eléonore Kessaci <>
Submitted on : Friday, July 21, 2017 - 1:01:06 PM
Last modification on : Tuesday, February 4, 2020 - 4:28:01 PM

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Marie-Eléonore Kessaci, Clarisse Dhaenens, Jérémie Humeau. Neutral Neighbors in Bi-objective Optimization: Distribution of the Most Promising for Permutation Problems. EMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization, Mar 2017, Munster, Germany. pp.344-358, ⟨10.1007/978-3-319-54157-0_24⟩. ⟨hal-01566859⟩

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