An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Towards a self-stopping evolutionary algorithm using coupling from the past
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multicriteria Optimization
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and 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
Usage of PSO algorithm for parameters identification of district heating network simulation model
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume II
A taxonomy of online stopping criteria for multi-objective evolutionary algorithms
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Usage of peak functions in heat load modeling of district heating system
ACMOS'11 Proceedings of the 13th WSEAS international conference on Automatic control, modelling & simulation
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In this work we put forward a comprehensive study on the design of global stopping criteria for multi-objective optimization. We describe a novel stopping criterion, denominated MGBM criterion that combines the mutual domination rate (MDR) improvement indicator with a simplified Kalman filter that is used for evidence gathering process. The MDR indicator, which is introduced along, is a special purpose solution meant for the stopping task. It is capable of gauging the progress of the optimization with a low computational cost and therefore suitable for solving complex or many-objective problems. The viability of the proposal is established by comparing it with some other possible alternatives. It should be noted that, although the criteria discussed here are meant for MOPs and MOEAs, they could be easily adapted to other softcomputing or numerical methods by substituting the local improvement metric with a suitable one.