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- W2159318397 abstract "Hybridizing of evolutionary algorithms (EA) by means of local search has shown considerable performance improvement in single-objective optimization (SOO) field. The fine search in the neighborhood of the EA individuals (solutions) allows a fine exploration of the solution space. This paper investigates the application and the evaluation of the hybridizing mechanism of the EAs in the multi-objective optimization (MOO) domain. For this hybridizing, two types of multi-objective local search (MOLS) are used; namely large- and narrow-MOLS. Among numerous possible multi-objective optimization EAs (MOEAs), the Pareto-based variants have been considered. The performance of hybrid MOO variants are evaluated by solving the multi-objective knapsack problem." @default.
- W2159318397 created "2016-06-24" @default.
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- W2159318397 date "2008-09-01" @default.
- W2159318397 modified "2023-09-25" @default.
- W2159318397 title "Hybridizing the Pareto Multi-Objective Optimization Evolutionary Algorithms by Means of Multi-Objective Local Search" @default.
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- W2159318397 doi "https://doi.org/10.1109/his.2008.104" @default.
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