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GECCO
2007
Springer

A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms

9 years 5 months ago
A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms
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. Evidence is 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 highdimensional problems where the traditional procedure of stopping after a predefined amount of iterations cannot be used and the waste of computational resources can induce to a detriment of the ...
Luis Martí, Jesús García, Ant
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors Luis Martí, Jesús García, Antonio Berlanga, José Manuel Molina
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