Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Covariance Matrix Adaptation for Multi-objective 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
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
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
A new multi-objective evolutionary algorithm based on a performance assessment indicator
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Population size matters: rigorous runtime results for maximizing the hypervolume indicator
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
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Indicator-based methods to tackle multiobjective problems have become popular recently, mainly because they allow to incorporate user preferences into the search explicitly. Multiobjective Evolutionary Algorithms (MOEAs) using the hypervolume indicator in particular showed better performance than classical MOEAs in experimental comparisons. In this paper, the use of indicator-based MOEAs is investigated for the first time from a theoretical point of view. We carry out running time analyses for an evolutionary algorithm with a (μ+ 1)-selection scheme based on the hypervolume indicator as it is used in most of the recently proposed MOEAs. Our analyses point out two important aspects of the search process. First, we examine how such algorithms can approach the Pareto front. Later on, we point out how they can achieve a good approximation for an exponentially large Pareto front.