An introduction to differential evolution
New ideas in optimization
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Knowledge-based solution to dynamic optimization problems using cultural algorithms
Knowledge-based solution to dynamic optimization problems using cultural algorithms
Optimization with constraints using a cultured differential evolution approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Towards estimating nadir objective vector using evolutionary approaches
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Minimal sets of quality metrics
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
An approximate ϵ-constraint method for a multi-objective job scheduling in the cloud
Future Generation Computer Systems
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In this paper we present the use of a previously developed single-objective optimization approach, together with the ε-constraint method, to provide an approximation of the Pareto front in a multiobjective optimization problem. This approximation is usually very near of the true Pareto front, but its cost grows with the desired number of points in the output set. As an alternative, it is possible to generate only a few points, and execute a second phase which will generate intermediate points, to increase the size of the output set. We use a rough sets-based approach for this second phase, which is a very robust approach. The results of this two-phase approach are very competitive in hard multiobjective problems, and is less expensive than the ε-constraint method alone. This approach is very effective on hard multiobjective problems, where is able to find good approximations of the Pareto front with less funtion evaluations than other approaches, as the NSGA-II (against which is compared).