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- W2506222777 abstract "Probabilistic modeling of selected solutions and incorporation of local search methods are approaches that can notably improve the results of multi-objective evolutionary algorithms (MOEAs). In the past, these approaches have been jointly applied to multi-objective problems (MOPs) with excellent results. In this paper, we introduce for the first time a joint probabilistic modeling of (1) local search methods with (2) decision variables and (3) the objectives in a framework named HMOBEDA. The proposed approach is compared with six evolutionary methods (including a modified version of NSGA-III, adapted to solve combinatorial optimization) on instances of the multi-objective knapsack problem with 3, 4, and 5 objectives. Results show that HMOBEDA is a competitive approach. It outperforms the other methods according to the hypervolume indicator." @default.
- W2506222777 created "2016-08-23" @default.
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- W2506222777 date "2016-07-20" @default.
- W2506222777 modified "2023-09-26" @default.
- W2506222777 title "HMOBEDA" @default.
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- W2506222777 doi "https://doi.org/10.1145/2908812.2908826" @default.
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