Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Rigorous runtime analysis of a (μ+1)ES for the sphere function
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
How the (1 + 1) ES using isotropic mutations minimizes positive definite quadratic forms
Theoretical Computer Science - Foundations of genetic algorithms
Algorithmic analysis of a basic evolutionary algorithm for continuous optimization
Theoretical Computer Science
Convergence rates of (1+1) evolutionary multiobjective optimization algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Comparison-based complexity of multiobjective optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
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Convergence rate analyses of evolutionary multi-objective optimization algorithms in continuous search space are yet rare. First results have been obtained for simple algorithms. Here, we provide concrete results of convergence rates for a state-of-the-art algorithm, namely the S-metric selection evolutionary multi-objective optimization algorithm (SMSEMOA). We show that the SMS-EMOA produces the same sequence of populations as certain single-objective evolutionary algorithms on arbitrary problem classes. Thereby we are able to transfer known convergence properties for classes of convex functions. We especially consider the SMS-EMOA with populations of parents and offspring greater than one and different concepts for the choice of the reference point used for the internal calculation of the dominated hypervolume within the selection operator.