A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
A Population Based Approach for ACO
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Multi-objective Optimisation Based on Relation Favour
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Solving multi-criteria optimization problems with population-based ACO
EMO'03 Proceedings of the 2nd 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
Compressed-objective genetic algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
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
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Two new ranking methods for solutions of multi objective optimization problems are proposed in this paper. Theoretical results show that both new ranking methods form a total preorder and are refinements of the pareto dominance relation. These properties make the ranking methods suitable for the selection of a subset of good solutions from a set of non-dominated solutions as needed by meta-heuristics. In particular, this is shown experimentally for a Population-based ACO that uses the ranking methods to solve a multi objective flow shop problem.