Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
An efficient multi-objective evolutionary algorithm with steady-state replacement model
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A Study of Convergence Speed in Multi-objective Metaheuristics
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Towards a quick computation of well-spread pareto-optimal solutions
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Constrained multi-objective optimization using steady state genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Steady-state selection and efficient covariance matrix update in the multi-objective CMA-ES
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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
Preference ranking schemes in multi-objective evolutionary algorithms
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
An evolutionary optimization approach for bulk material blending systems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Proceedings of the 2013 International Conference on Software Engineering
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Genetic Algorithms (GAs) have been widely used in single-objective as well as in multi-objective optimization for solving complex optimization problems. Two different models of GAs can be considered according to their selection scheme: generational and steady-state. Although most of the state-of-the-art multi-objective GAs (MOGAs) use a generational scheme, in the last few years many proposals using a steady-state scheme have been developed. However, the influence of using those selection strategies in MOGAs has not been studied in detail. In this paper we deal with this gap. We have implemented steady-state versions of the NSGA-II and SPEA2 algorithms, and we have compared them to the generational ones according to three criteria: the quality of the resulting approximation sets to the Pareto front, the convergence speed of the algorithm, and the computing time. The results show that multi-objective GAs can profit from the steady-state model in many scenarios.