An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A stopping criterion based on Kalman estimation techniques with several progress indicators
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Statistical methods for convergence detection of multi-objective evolutionary algorithms
Evolutionary Computation
Multi-criteria service selection with optimal stopping in dynamic service-oriented systems
ICDCIT'10 Proceedings of the 6th international conference on Distributed Computing and Internet Technology
Self-adaptation techniques applied to multi-objective evolutionary algorithms
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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In this work we present a novel and efficient algorithm independent stopping criterion, called the MGBM criterion, suitable for Multiobjective Optimization Evolutionary Algorithms (MOEAs). The criterion, after each iteration of the optimization algorithm, gathers evidence of the improvement of the solutions obtained so far. A global (execution wise) evidence accumulation process inspired by recursive Bayesian estimation decides when the optimization should be stopped. Evidenceis collected using a novel relative improvement measure constructed on top of the Pareto dominance relations. The evidence gathered after each iteration is accumulated and updated following a rule based on a simplified version of a discrete Kalman filter. Our criterion is particularly useful in complex and/or high-dimensional problems where the traditional procedure of stopping after a predefined amount of iterations cannot be used and the waste of computational resources can induceto a detriment of the quality of the results. Although the criterion discussed here is meant for MOEAs,it can be easily adapted to other soft computing or numerical methods by substituting the local improvement metric with a suitable one.