On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
On the effect of populations in evolutionary multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
Do additional objectives make a problem harder?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Approximating covering problems by randomized search heuristics using multi-objective models
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Benefits and drawbacks for the use of epsilon-dominance in evolutionary multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Illustration of fairness in evolutionary multi-objective optimization
Theoretical Computer Science
Information Sciences: an International Journal
An analysis on recombination in multi-objective evolutionary optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the effect of connectedness for biobjective multiple and long path problems
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Information Sciences: an International Journal
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Often the Pareto front of a multi-objective optimization problem grows exponentially with the problem size. In this case, it is not possible to compute the whole Pareto front efficiently and one is interested in good approximations. We consider how evolutionary algorithms can achieve such approximations by using different diversity mechanisms. We discuss some well-known approaches such as the density estimator and the ε-dominance approach and point out how and when such mechanisms provably help to obtain good additive approximations of the Pareto-optimal set.