Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Objective reduction using a feature selection technique
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Controlling dominance area of solutions and its impact on the performance of MOEAs
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Robust multi-objective optimization in high dimensional spaces
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Quantifying the effects of objective space dimension in evolutionary multiobjective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
Some techniques to deal with many-objective problems
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A grid-based fitness strategy for evolutionary many-objective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An approach based on grid-value for selection of parents in multi-objective genetic algorithm
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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This paper presents a quantitative analysis of different preference relations proposed to deal with problems with a high number of objectives. Since the relations stress different subsets of the Pareto front, we based the comparison on the Tchebycheff distance of the approximation set to the "knee" of the Pareto front. Additionally, the convergence induced by the preference relations is studied by analyzing the generational distance observed at each generation of the search. The results show that some preference relations contribute to converge quickly to the Pareto front, but they promote the generation of solutions far from the knee region. Moreover, even if a preference relation generates solutions near the knee, there exists a trade-off between convergence and the extension of the Pareto front covered.