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Multi-objective NK landscapes with heterogeneous objectives


So far, multi-objective NK landscapes have been investigated under the assumption of a homogeneous nature of the involved objectives in terms of difficulty. However, we argue that problems with heterogeneous objectives, e.g., in terms of multi-modality, can be challenging for multi-objective evolutionary algorithms, and deserve further considerations. In this paper, we propose a model of multi-objective NK landscapes, where each objective has a different degree of variable interactions (K), as a benchmark to investigate heterogeneous multi-objective optimization problems. We show that the use of a rank-annotated neighborhood network with labeled local optimal solutions, together with landscape metrics extracted from the heterogeneous objectives, thoroughly characterize bi-objective NK landscapes with a different level of heterogeneity among the objectives.
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hal-03693674 , version 1 (02-03-2023)



Raphaël Cosson, Roberto Santana, Bilel Derbel, Arnaud Liefooghe. Multi-objective NK landscapes with heterogeneous objectives. GECCO 2022 - Genetic and Evolutionary Computation Conference, 2022, Boston, MA, United States. pp.502-510, ⟨10.1145/3512290.3528858⟩. ⟨hal-03693674⟩
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