On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
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
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
Approximating covering problems by randomized search heuristics using multi-objective models
Proceedings of the 9th annual conference on Genetic and 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
Computing minimum cuts by randomized search heuristics
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Analyzing Hypervolume Indicator Based Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Runtime Analyses for Using Fairness in Evolutionary Multi-Objective Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Additive approximations of pareto-optimal sets by evolutionary multi-objective algorithms
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Don't be greedy when calculating hypervolume contributions
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Approximating the Least Hypervolume Contributor: NP-Hard in General, But Fast in Practice
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Multiplicative approximations and the hypervolume indicator
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
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It is widely assumed that evolutionary algorithms for multi-objective optimization problems should use certain mechanisms to achieve a good spread over the Pareto front. In this paper, we examine such mechanisms from a theoretical point of view and analyze simple algorithms incorporating the concept of fairness. This mechanism tries to balance the number of offspring of all individuals in the current population. We rigorously analyze the runtime behavior of different fairness mechanisms and present illustrative examples to point out situations, where the right mechanism can speed up the optimization process significantly. We also indicate drawbacks for the use of fairness by presenting instances, where the optimization process is slowed down drastically.