Computing budget allocation in multi-objective evolutionary algorithms for stochastic problems

Mengmei Liu, Aaron M. Cramer

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Multi-objective stochastic problems are important problems in practice and are often solved through multi-objective evolutionary algorithms. Researchers have developed different noise handling techniques to improve the efficiency and accuracy of such algorithms, primarily by integrating these methods into the evaluation or environmental selection steps of the algorithms. In this work, a combination of studies that compare integration of different computing budget allocation methods into either the evaluation or the environmental selection steps are conducted. These comparisons are performed on stochastic problems derived from benchmark multi-objective optimization problems and consider varying levels of noise. The algorithms are compared in terms of both proximity to and coverage of the true Pareto-optimal front and sufficient studies are performed to allow statistically significant conclusions to be drawn. It is shown that integrating computing budget allocation methods into the environmental selection step is better than integration within the evaluation step.

Original languageEnglish
Pages (from-to)267-274
Number of pages8
JournalSwarm and Evolutionary Computation
StatePublished - Feb 2018

Bibliographical note

Publisher Copyright:
© 2017 Elsevier B.V.


  • Computational complexity
  • Evolutionary computation
  • Gaussian noise
  • Genetic algorithms
  • Pareto analysis

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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