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- W2338706966 abstract "AbstractMulti-objective evolutionary algorithms (MOEAs) are currently a dynamic field of research that has attracted considerable attention. Mutation operators have been utilized by MOEAs as variation mechanisms. In particular, polynomial mutation (PLM) is one of the most popular variation mechanisms and has been utilized by many well-known MOEAs. In this paper, we revisit the PLM operator and we propose a fitness-guided version of the PLM. Experimental results obtained by non-dominated sorting genetic algorithm II and strength Pareto evolutionary algorithm 2 show that the proposed fitness-guided mutation operator outperforms the classical PLM operator, based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it." @default.
- W2338706966 created "2016-06-24" @default.
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- W2338706966 date "2016-02-10" @default.
- W2338706966 modified "2023-09-25" @default.
- W2338706966 title "Enhancing the performance of MOEAs: an experimental presentation of a new fitness guided mutation operator" @default.
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- W2338706966 doi "https://doi.org/10.1080/0952813x.2015.1132260" @default.
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