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A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms

Published: 07 July 2007 Publication History

Abstract

In this work we present a novel and efficient algorithm independent stopping criterion, called the MGBM criterion,suitable for Multi-objective 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 high-dimensional problems where the traditional procedure of stopping after a predefined amount of iterations cannot beused 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 witha suitable one.

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2007

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Author Tags

  1. MOEAs
  2. stopping criterion

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2016)A stopping criterion for multi-objective optimization evolutionary algorithmsInformation Sciences: an International Journal10.1016/j.ins.2016.07.025367:C(700-718)Online publication date: 1-Nov-2016
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