Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multi-objective Optimisation Based on Relation Favour
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Towards a quick computation of well-spread pareto-optimal solutions
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
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
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
An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems
Annals of Mathematics and Artificial Intelligence
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Pareto dominance (PD) has been the most commonly adopted relation to compare solutions in the multiobjective optimization context. Multiobjective evolutionary algorithms (MOEAs) based on PD have been successfully used in order to optimize bi-objective and three-objective problems. However, it has been shown that Pareto dominance loses its effectiveness as the number of objectives increases and thus, the convergence behavior of approaches based on this concept decreases. This paper tackles the MOEAs' scalability problem that arises as we increase the number of objective functions. In this paper, we perform a comparative study of some of the state-of-the-art fitness assignment methods available for multiobjective optimization in order to analyze their ability to guide the search process in high-dimensional objective spaces.