Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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
On the hardness of offline multi-objective optimization
Evolutionary Computation
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
Convergence rates of SMS-EMOA on continuous bi-objective problem classes
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
A taxonomy of online stopping criteria for multi-objective evolutionary algorithms
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Convergence of set-based multi-objective optimization, indicators and deteriorative cycles
Theoretical Computer Science
Information Sciences: an International Journal
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Convergence analyses of evolutionary multiobjective optimization algorithms typically deal with the convergence in limit (stochastic convergence) or the run time. Here, for the first time concrete results for convergence rates of several popular algorithms on certain classes of continuous functions are presented. We consider the algorithms in the version of using a (1+1) selection scheme. Then, SMS-EMOA and IBEAε+ achieve linear convergence rate, proved by showing algorithmic equivalence to the single-objective (1+1)-EA with self-adaptation, whereas NSGA-II and SPEA2 have a sub-linear convergence rate, proved by reducing them to a multiobjective algorithm with known properties.